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Week 11 Role of informatics in promoting patient centered care

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Week 11 Role of informatics in promoting patient centered care

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146983/

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Week 11  How to keep your patient's health information confidential.

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Week 11  How to keep your patient's health information confidential.

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https://healthitsecurity.com/news/best-practices-for-keeping-patient-data-confidential

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Week 11 tips on how to keep your patient's data safe

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Week 11 tips on how to keep your patient's data safe

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https://compliancy-group.com/10-tips-for-protecting-patient-health-information-in-the-workplace/

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10 Tips for Protecting Patient Health Information in the Workplace

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Take steps to minimize risk, provide training, and be mindful to protect PHI. Compliancy Group can help you with HIPAA tips for staff in the workplace.

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Week 11 How Employees can help prevent HIPAA violations

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Week 11 How Employees can help prevent HIPAA violations

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https://www.hipaajournal.com/employees-prevent-hipaa-violations/

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Share this article on:

Healthcare organizations and their business associates must comply with the HIPAA Privacy, Security, and Breach Notifications Rules and implement safeguards to prevent HIPAA violations. However, even with controls in place to reduce the risk of HIPAA violations, data breaches still occur.

In most industries, it is hackers and other cybercriminals that are responsible for the majority of security breaches, but in healthcare it is insiders. While healthcare organizations can take steps to improve their defenses and implement technologies to identify breaches rapidly when they occur, healthcare employees also need to help prevent HIPAA violations. Employers can help employees by providing regular HIPAA training.

Employees Can Help to Prevent HIPAA Violations

Healthcare privacy breaches often occur as a result of carelessness or a lack of understanding of HIPAA Rules. Healthcare organizations should therefore ensure employees receive full training on HIPAA and know the allowable uses and disclosures of PHI and to secure ePHI at all times. Refresher training sessions should also be provided regularly to ensure HIPAA Rules are not forgotten.

Employees must also take responsibility for HIPAA compliance and help to prevent HIPAA violations. Even relatively minor violations of HIPAA Rules can have severe consequences. Organizations can be liable for substantial fines, HIPAA violations can cause damage to organizations' reputations, as well as harm to patients. Employees discovered to have violated HIPAA Rules, even accidentally, face termination and in severe cases, may face criminal charges.

As an employee, if you want to prevent HIPAA violations, take a look at the HIPAA violations below and make sure you do not make these common errors.

How Employees Can Prevent HIPAA Violations

Listed below are some of the common ways HIPAA Rules are violated by employees.

Never Disclose Passwords or Share Login Credentials

Every employee is provided with a unique login, through which they will be granted access to sensitive information. It is therefore essential that those login details remain private. Login credentials should never be shared or written down. Login information is used to track the actions of users, including activities involving ePHI. If another employee has your login credentials, and improperly accesses ePHI using those credentials, it will be your job that is on the line.

Never Leave Portable Devices or Documents Unattended

The Office for Civil Rights breach portal is littered with reports of data breaches involving lost and stolen devices and mishandled PHI. A lost or stolen device containing ePHI is reportable under HIPAA Rules if the device is not encrypted. The Office for Civil Rights investigates reports of lost and stolen devices to determine if HIPAA Rules have been violated. If those devices are discovered to have been left unattended, financial penalties may be issued. Portable devices must never be left unattended and when in use.

The same applies to paper records. Even when busy, healthcare employees must never leave documents containing PHI in areas where they can be viewed by unauthorized individuals, picked up by other healthcare workers, or seen by other patients.

You can prevent HIPAA violations by reminding employees who are not taking sufficient care with patient files about the risk of accidental disclosures of PHI.

Do Not Text Patient Information

Text messages are a quick and easy way to communicate, whether via the SMS network, WhatsApp, or Facebook Messenger. Unfortunately, none of the common messaging services have the necessary controls to prevent accidental disclosures of ePHI to unauthorized individuals.

For example, SMS messages are not encrypted and can easily be intercepted. WhatsApp is encrypted, but lacks appropriate authentication controls. In order for a text messaging service to be used, your employer must have signed a HIPAA-compliant business associate agreement with the service provider. If you need to send ePHI, only do so through approved channels such as a secure, healthcare text messaging platform.

Don't Dispose of PHI with Regular Trash

While most healthcare organizations have now transitioned to electronic health records, documents are still widely used. Any document containing the PHI of a patient must be kept secure at all times and disposed of securely when no longer required. HIPAA requires all PHI to be rendered unreadable, indecipherable, and unable to be reconstructed when it is no longer needed. Your employer should have strict rules covering the disposal of PHI which prohibits the disposal of documents with regular trash. You must be extremely careful to ensure that any paper copies of PHI are disposed of securely.

Never Access Patient Records Out of Curiosity

The accessing of patient health records by employees, without any legitimate reason for doing so, is a serious violation of HIPAA Rules and patient privacy. While the majority of healthcare employees respect the privacy of patients, there have been numerous cases over the years of patients snooping on the records of patients.

Healthcare employees are only permitted to view patient records if they are required to do so for treatment, payment and healthcare operations. For treatment purposes, employees are only permitted to view the records of their own patients.

The HIPAA Security Rule requires covered entities to maintain access logs to ensure inappropriate ePHI access can be identified. Those logs must be regularly reviewed. Depending on the system in place, a flag could be immediately raised or it may take until the next audit for the privacy violation to be discovered, but Improper accessing of PHI will be identified.

If medical records are accessed without authorization it is likely to result in termination, and potentially criminal penalties against the individual concerned. Such actions are also likely to make it difficult to obtain future employment at other healthcare organizations. Your employer can also face heavy fines and considerable reputation damage.

Don't Take Medical Records with You When You Change Job

When employees leave a practice, they can be tempted to take PHI with them. Some new employers may even encourage this - the information could be used to recruit patients or sell them medical services or equipment. However, taking medical records, even if there has been a longstanding relationship with the patient, is data theft and could result in criminal charges.

Don't Access Your Own Medical Records Using Your Login Credentials

The HIPAA Privacy Rule allows patients to obtain copies of their health records on request, but healthcare employees do not have the right to access their medical records using their login credentials. Typically, healthcare providers require staff to go through the same process as patients. In order to gain access to their health data, they must submit a request for a copy of their health information via their HIM department.

Do Not Share ePHI on Social Media (Including Photos)

Many healthcare organizations have developed policies covering the use of social media by their employees and clearly state that details of work activities should not be shared via social media accounts. The sending of a tweet containing personally identifiable information of a patient is a serious HIPAA violation. The same applies to posting on Facebook, even in a closed Facebook group. That includes ePHI and gossip about a patient.

PHI includes health information, but also photographs and videos. In such cases, it doesn't matter if the photograph does not include the patients name. Patients could easily be identified from the photograph.

Selfies taken at work and posted to social media accounts would violate HIPAA Rules if patients are included in the photograph if prior consent has not been obtained in writing. It would also be a HIPAA violation if PHI can be seen in the photographs - documents and charts etc. If in any doubt about HIPAA Rules, don't post on social media without speaking to your compliance officer. The National Council of State Boards of Nursing (NCSBN) has published a useful guide for nurses on the use of social media.

There have been several high-profile cases of nurses and other healthcare employees taking photographs or videos of patients and uploading them to social media accounts. Inappropriate sharing of PHI can attract significant financial penalties for the covered entity, termination of employment contracts, loss of licenses, and lawsuits.

Report Potential HIPAA Violations

If you believe a colleague has violated HIPAA Rules it is important to take action to prevent similar incidents from occurring in the future. Report potential HIPAA violations internally to your compliance officer so that action can be taken promptly to address the problem.

If you believe your organization is not doing enough to prevent HIPAA violations, consult your compliance officer. If HIPAA Rules are being regularly violated, you can file a complaint with the HHS' Office for Civil Rights.

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How Employees Can Help Prevent HIPAA Violations

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Share this article on: Healthcare organizations and their business associates must comply with the HIPAA Privacy, Security, and Breach Notifications Rules and implement safeguards to prevent HIPAA violations. However, even with controls in place to reduce the risk of HIPAA violations, data breaches still occur.

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Week 11 Health information privacy, confidentiality, and security

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Week 11 Health information privacy, confidentiality, and security

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https://journalofethics.ama-assn.org/article/electronic-health-records-privacy-confidentiality-and-security/2012-09

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Week 11 Health info privacy laws in the digital age

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Week 11 Health info privacy laws in the digital age

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883355/

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Health Information Privacy Laws in the Digital Age: HIPAA Doesn’t Apply

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The notion of health information privacy has evolved over time as the healthcare industry has embraced technology. Where once individuals were concerned about the privacy of their conversations and financial information, the digitization of health data…

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Week 11 Three rules of Informatics HIPPA

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Week 11 Three rules of Informatics HIPPA

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The Health Insurance Portability and Accountability Act (HIPAA) lays out three rules for protecting patient health information, namely: The Privacy Rule. The Security Rule. The Breach Notification Rule.

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Protecting patient privacy and securing electronic health information is a shared responsibility. Adherence to privacy and security standards fosters patient trust. It assures patients that their electronic health information — while under your control — will remain confidential, accurate, and secure.

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https://www.academia.edu/13510672/Administrative_claims_data_analysis_of_nurse_practitioner_prescribing_for_older_adults

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Administrative claims data analysis of nurse practitioner prescribing for older adults

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Administrative claims data analysis of nurse practitioner prescribing for older adults

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https://www.sciencedirect.com/science/article/abs/pii/S8755722320300788

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The use of big data and data mining in nurse practitioner clinical education

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Nurse practitioner (NP) faculty have not fully used data collected in NP clinical education for data mining. With current advances in database technol…

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https://viehealthcare.com/how-data-analytics-is-used-in-healthcare/

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Table of Contents

The following topics are thoroughly covered in this article:

1. What is healthcare analytics?

2. Examples of analytics in healthcare

3. How analytics benefits providers, patients and payers

4. How data analytics can support your hospital or healthcare systems' cost savings and financial improvement goals

What is Healthcare Analytics?

Analytics is becoming increasingly prevalent across nearly every industry, which is a trend that's notably apparent within the healthcare industry. The provision of modern treatment methods and techniques relies on data analytics. When implemented correctly, analytics can predict outbreaks, bolster preventive care, enhance the overall quality of care that patients receive, and lessen the costs associated with treatments.
When you hear the word "analytics", this means that an ample amount of data is being digitized and further consolidated, which helps with analysis of the data. In disparate pieces, the data may not mean much. However, when put together, the information can tell a story that allows healthcare professionals to treat illnesses early or identify the initial warning signs for a disease.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Healthcare analytics involves numerous intricacies that differ from other industries. For instance, real-time vital signs and electronic health records are taken into account when performing analytics. This data can be added to social determinants of health data to proactively manage patients when they leave a hospital. Because of the complex processes involved with healthcare analytics, an agile analytics solution is necessary that is supported by subject matter healthcare experts.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Click To Tweet

Examples of Analytics in Healthcare

To better understand healthcare analytics and all that it entails, it's important to take a look at various examples of analytics in healthcare. The comprehensive data that can be collected through healthcare analytics can be converted into numerous insights. The following are some top examples of how analytics can be used within the healthcare industry.

Preventive Care

The key to preventive care is to identify risk factors in someone who may be susceptible to a specific condition or disease. For instance, anyone who smokes cigarettes on a regular basis will have a higher risk of developing lung diseases like COPD, asthma, and lung cancer. Healthcare analytics can be used to effectively identify risk factors that might not have previously been tied to the disease or condition in question.
Back in 2017, Blue Cross Blue Shield analyzed many years of pharmacy and insurance data pertaining to opioid abuse and overdose. Through this analysis, they were able to identify nearly 750 risk factors that accurately predict if someone is at risk of abusing opioids. Gathering all of this data was only possible with the help of analytics experts and the right software solutions.

In the same vein as preventive care, data analytics is also being used for predictive purposes. Companies like Optum Labs have collected electronic health records of millions of patients, which have then been stored in a database. From this database, it's possible to use the data to strengthen patient care and make it more efficient. When analytics are used in a healthcare setting, doctors can make immediate decisions that are driven largely by data that's available through analytics. In certain situations, this data can be used to predict the initial onset or eventual progression of various conditions.

Staffing

One manner in which hospital data analytics can be highly beneficial is with staffing. Without the use of analytics, staffing issues can result from under-staffing or over-staffing. When the hospital is under-staffed, the patients aren't provided with the level of care that they require. On the other hand, over-staffing issues result in higher expenses and a loss of money for the hospital. Using data analytics allows your hospital to successfully avoid such problems by analyzing months or years of hospital admissions records.
When healthcare data analytics is performed on these records, it's possible to identify patterns and accurately predict what admissions will be like at the hospital around 7-14 days in advance. This data can then be used to schedule a higher amount of staff during times when greater admissions are expected. This should lead to less wait times for the patient as well as better overall care.

Patient Engagement Improvements

Using data analytics, healthcare has been shown to improve patient engagement as well. A large number of prospective patients wear smart devices that are able to track numerous metrics like sleep patterns, heart rates, and number of steps taken. Through these metrics, patients are monitoring their overall health. It's possible for the data that's collected from these devices to be uploaded to the cloud, which provides doctors and physicians with the ability to look over the data and draw conclusions from it.
If a patient suffers from hypertension or asthma, the data that's sent to the physician from the wearable device can be used to identify the proper treatment plan and to make sure that the patient is taking the steps necessary to correct their symptoms and lessen their need for medical care. When used correctly, patients should be able to reduce the number of visits that they need to take to their primary doctor. The doctor could also identify a potential risk factor or problem with the data that requires further treatment on an in-person basis, which could allow for preventive care to be administered.

Electronic Health Records

Hospital data analytics is mainly used for electronic health records, which are currently kept by nearly 95 percent of all hospitals across the U.S. A digital health record is an extensive record that includes even the smallest of details pertaining to the patient's health. Along with standard demographic information and medical histories of family members, electronic health records also document allergies, medications, test results, and past diagnoses.
Each record is comprised of a single file, which makes it easy to avoid duplicate records and excess paperwork. These records can be kept for many years without needing to worry about a loss of data or similar problems that could result in incomplete records. Electronic health records are only possible with data analytics healthcare, which provides hospitals with a simple tool for the collection and analysis of copious amounts of data.
Keep in mind that a core benefit of electronic health records is that they can be instantly accessed by different healthcare providers and hospitals, which eliminates the need for sending patient information from one medical facility to another. With the kind of data that's collected in an EHR, lab tests, and office visits, a patient's healthcare can be optimized.

How HealthCare Data Analytics is Used by Medical Providers

In the past, most healthcare providers relied on a fee-for-service model that was straightforward and efficient in practice, but had its drawbacks. With this particular model, doctors received very little incentive to provide value-based care that was more holistic in nature.
Without using hospital data analytics, the value-based care model can be more inefficient than its fee-based counterpart. Hospitals are required to improve efficiency and day-to-day operations with the value-based model, which is where analytics is used. When an analytics solution is implemented, medical providers should be able to:

Improve performance with quality care that's backed up by data

Enhance quality of care and patient satisfaction by automating processes like processing insurance and making appointments

Perform predictive analytics for at-risk patients, which lessens readmission rates

When looking specifically at patient care, data analytics healthcare can use patient histories and patterns to determine which treatments are necessary. Predictive analytics can hone in on patients who currently suffer from chronic conditions and may be more at risk of being involved in an emergency medical situation. The decisions that are made from this data can increase corrective care while decreasing the number of emergency visits.
Keep in mind that human error is another significant problem that hospitals face when administering medical care. The majority of insurance claims that are successfully filed against a hospital are brought about by human error, which could involve anything from an incorrect diagnosis to the wrong dosage with a medication.
When these errors occur, hospitals and other healthcare facilities will be tasked with absorbing some of the claims and insurance costs that arise. Hospital data analytics can look over patient data and any prescribed medication to alert doctors and patients of incorrect dosages or wrong prescriptions, which lessens human error and the cost to your hospital.

How Healthcare Data Analytics Helps with Population Health Management

With population health management, a greater focus is being placed on predictive and preventive care as opposed to reactive care that centers around a response and treatment. When predictive analytics are implemented, a chronic illness could be identified while still in the early stages. Catching illnesses early on reduces the costs of care and lessens the possibility of repeat hospitalization. Some of the data that's collected and analyzed with data analytics healthcare extends to claims data, health data, social factors, and the results from lab testing.
When large amounts of data are collected and analyzed, healthcare data analytics can:

Measure potential patient outcomes to determine which treatments and programs have the highest likelihood of success

Track patient satisfaction and provider performance to better help with resource allocation

Provide the correct patient/provider ratios depending on the situation and number of admissions

The use of analytics in healthcare assists with keeping financial mismanagement at bay. By reducing over-staffing and under-staffing issues, patients should be provided with the appropriate level of care without long wait times. More efficient care equates to higher patient satisfaction and lower bed shortages.

How data analytics can support your hospital or healthcare systems cost savings and financial improvement goals

These are four ways a hospital can utilize data analytics to achieve cost savings:
1. Run 12 to 18 month PO detail report and benchmark each line item

1. This should be performed on a quarterly basis because pricing does change frequently on supply and implant expenses and because new products are utilized; this is a great way to identify off contract spend before it increases your costs unnecessarily.

2. The key to getting best pricing is to have a constant quarterly monitoring review and even from 2 different benchmarking resources; different companies will have access to different data and you want to see all of the opportunities available to your organization.

2. Identify utilization improvement opportunities through comparing physician variations in testing, supplies and specifically implant costs in the O.R.

1. Physician engagement is a leading (and underutilized) strategy for hospitals to deploy.

2. Given the right data and understanding of the benchmarks, they will be one of the best resources your hospital can utilize for cost savings.

3. Analyze your professional hourly rates to identify ways to reduce these outsourced hourly fees and standardize throughout your hospital and health system.

1. Organize hourly rates for professional services by specific category and have a one hourly rate allowable for specific services. Project management is one hourly rate, technical analyst is another rate, etc.

2. Engage an outside expert resource to provide you with accurate and competitive hourly rates. Most organizations have the ability to save over 20% in this area of spend with a focused initiative on hourly professional services rates and having the right benchmarking partner to achieve better pricing.

3. Take an area of spend in your purchased services, like regulated medical waste or medical records or specialty reference lab, and analyze the last 12 months of invoice line item spend for utilization improvement, invoice accuracy to the agreement, and pricing opportunities that can be achieved through an experienced benchmarking resource.

1. Since services spend is detailed only on an invoice, the best approach is to work with a company, like VIE Healthcare, who has patented technology to extract and analyze your purchased services invoice line item details.

2. Review ways to reduce utilization or consolidate if you have multiple service vendors providing the same service.

At VIE Healthcare Consulting, we focus on delivering non-labor cost savings. We are the preferred advisory solution for hospitals and healthcare systems to increase operating margins, reduce costs, and improve efficiencies.
We are a team of margin improvement experts, cost consultants, healthcare business analysts, data scientists, technology specialists, and clinicians. We deliver real cost savings and financial results - since 1999 we have saved our clients over $1 Billion in non-salaried expenses.
If you are interested in learning more, the quickest way to get your questions answered is to contact one of our margin improvement experts or email Lisa Miller at lmiller@spendmend.com.

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How Data Analytics is Used in Healthcare

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An in-depth guide to how data analytics is being used in healthcare settings and how analytics can benefit your hospital or healthcare facility.

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https://www.comptia.org/content/articles/how-is-data-analytics-used-in-health-care

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Data analytics is the process of analyzing raw data for the purpose of determining trends and enabling better decision making. It is relevant to all types of organizations, especially health care organizations.

Data analytics in health care is vital. It helps health care organizations to evaluate and develop practitioners, detect anomalies in scans and predict outbreaks in illness, per the Harvard Business School. Data analytics can also lower costs for health care organizations and boost business intelligence. Most importantly, it helps health care companies to make better care decisions for patients.

What Are the Types of Healthcare Analytics?

There is more than one type of health care analytics. The top categories of data analytics in health care include:

Descriptive analytics: Descriptive analytics in health care uses historical patient data to glean insights into benchmarks and trends.

Prescriptive analytics: Prescriptive analytics relies on machine learning to propose a strategy.

Predictive analytics: In health care, predictive analytics uses both forecasting and modeling to predict what will probably happen in the future.

Discovery analytics: Like prescriptive analytics, discovery analytics also uses machine learning. The difference is, it utilizes machine learning to examine clinical data for the purpose of determining patterns that provide actionable insights.

Different analytics tools are used for each of these categories of health care analytics. These types of data analytics can be used in practical ways to benefit health care organizations, as well as patients.

How Health Care Data Analytics is Used by Medical Providers: Examples of Data Analytics in Healthcare

The best way to discover how data is used in health care is to take a look at real-life examples of big data analytics in healthcare. Vie Healthcare Consulting tells the story of how data analytics informs preventative care.

Preventative care is essential for health care systems and patients. It can help prevent future illness and patient readmissions to health systems. It can also promote better patient outcomes and lower health insurance costs. This is especially true of high-risk patients and those with chronic diseases. Cancer screenings, well-child visits and counseling on smoking cessation are all examples of preventative care. By identifying risk factors that could have gone unnoticed, health care analytics can be used to promote better preventative care.

Lisa Miller, contributor to the Vie Healthcare Consulting blog, gives an example of how health care analytics can promote preventative care through insurance companies.

Miller explains that in 2017, Blue Cross Blue Shield analyzed several years of pharmacy and insurance data. The data was related to opioid abuse and overdose. Through the analysis, Blue Cross Blue Shield was able to effectively identify almost 750 risk factors that can predict whether or not someone is at risk of abusing opioids.

"Gathering all of this data was only possible with the help of analytics experts and the right software solutions," Miller said.

How Data Analytics in Health Care Improves Patient Care

One of the most amazing things about data analytics in health care is that it enables health systems and clinicians to make better care decisions for patients.

"In health care, decisions often have life-altering outcomes-both for patients and the population as a whole," said Catherine Cote in the same Harvard Business School article from above. "The ability to quickly gather and analyze complete, accurate data enables decision makers to make choices regarding treatment or surgery, predict the path of large-scale health events and plan long-term."

Data analytics is helpful to health care professionals and organizations. Both health care providers and health systems need health information and data that makes sense. Without accurate data, they can't make decisions that are in patients' best interests. Data analytics provides these institutions with the data they need to make decisions that lead to superior patient care. This not only improves patients' quality of life, but can also extend their life.

Health Care Data Analytics Helps with Population Health Management

Health care data analytics not only improves patient care, it also helps with population health management. Population health management is the process of upgrading clinical outcomes of a group of people via better care coordination. Improved patient engagement is also a part of this process.

Health care data analytics can help with population health management. How? By enabling data scientists to build predictive artificial intelligence (AI) models. These models enable health care organizations to manage initiatives in the health of select populations. This is primarily done through identifying the health care system's most vulnerable patients.

"With these patients identified, organizations can perform outreach and interventions to maximize the quality of patient care and further enhance the AI model's effectiveness," according to an article by Health Catalyst.

This is another example of how data analytics can improve patients' lives and maximize the efficiency of health systems.

What Is the Future of Data Analytics in Health Care?

Like data analytics in all sectors, there is a solid future for data analytics in health care. This is particularly true in light of the COVID-19 pandemic.

Data analytics in health care has grown in importance during the pandemic. Hundreds of thousands of individuals around the world have required health care for treatment of the coronavirus. Health care organizations have utilized data analytics to manage the global health crisis and better treat patients.

The need for quality healthcare will remain constant. For this reason, data analytics in health care will always be relevant, and jobs in this field will remain in-demand. For example, the Journal of Ahima states that while the demand for jobs in data analysis is high in every industry, the most emerging role is that of health care data analyst.

Get a Job in Data Analytics in Health Care with CompTIA Data+

Data analytics is a great field to get into. Besides being in-demand, data-related jobs often pay well. On average, a job in data analytics in health care pays a median wage of $86,806.

Would you be a good health care data analyst? If you are interested in providing data solutions for health care companies and have a degree in business analytics, health IT or health information management (HIM), it could be. Some companies look for health data analysts with degrees or equivalent experience in statistics, data science and computer science. Some experience with electronic health record (EHR) data is also a huge plus to employers.

These in-demand data mining in healthcare jobs calls for specialized skills in data. If you need these skills and already have experience working in data, CompTIA Data+ certification training can prepare you for a role in this field.

CompTIA Data+, which will be available in Q1 of 2022, offers a full training suite of Official CompTIA CertMaster products. These products include:

CertMaster Learn: CertMaster Learn provides comprehensive eLearning that prepares you for the CompTIA Data+ certification exam.

CertMaster Labs: CertMaster Labs provides hands-on experience in real virtual environments.

CertMaster Practice: CertMaster Practice is an online knowledge assessment and certification exam practice and preparation companion tool.

When learners buy a training bundle that includes both CertMaster Learn and CertMaster Labs, they will enjoy an integrated training experience. Find out more in The New CompTIA eLearning Bundles: Where Knowledge and Practice Intersect.
Need more information about your online IT training options with CompTIA? Check this out.
After training with CompTIA Data+ certification resources, you'll be ready to take the certification exam. The exam covers:

Mining data

Manipulating data

Applying basic statistical methods

Analyzing complex data sets

To get the job you want, you have to prove to employers that you have the skills needed to do the job. With CompTIA Data+, you can do exactly that.
Interested in helping provide analytics solutions for health care organizations, or in working with data in general? Read more about Data and Analytics. Sign up to be the first to hear about a free trial of CertMaster Learn for CompTIA Data+.

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how is data analytics used in health care?

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How Is Data Analytics Used in Health Care | CompTIA

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Data analytics is the process of analyzing raw data for the purpose of determining trends and enabling better decision making. It is relevant to all types of organizations, especially health care organizations. Data analytics in health care is vital.

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https://journals.lww.com/nursingresearchonline/Fulltext/2006/03001/Data_Mining_as_a_Tool_for_Research_and_Knowledge.00009.aspx?casa_token=OJZYXCfNkLMAAAAA:DDa7rxQIX_d6M8ftNUFP2uqXlqqpRh8_mN3GsOelc9WCFuWBGyKM0aeE4xLg3D9jLUN6j7jVSR4bwBoU8QqSmw

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Tracy Magee, PhD, RN, CPNP, is Postdoctoral Fellow, Department of Maternal Child Nursing, University of Illinois at Chicago.

Susan M. Lee, PhD, RN, NP-C, is Clinical Nurse Specialist, Knight Nursing Center for Clinical and Professional Development, Massachusetts General Hospital, Boston.

Karen K. Giuliano, PhD, RN, FAAN, is Clinical Research Scientist, Philips Medical Systems, Andover, Massachusetts.

Barbara Munro, PhD, RN, FAAN, is Dean and Professor, William F. Connell School of Nursing Boston College, Chestnut Hill, Massachusetts.

Accepted for publication December 14, 2005.

Corresponding author: Tracy Magee, PhD, RN, CPNP, Department of Maternal Child Nursing, University of Illinois at Chicago, Chicago, IL 60637 (email: ).

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Generating New Knowledge From Existing Data: The Use of... : Nursing Research

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Objective: To explore the spectrum of methodological issues and practical applications encountered by three nurse researchers using secondary data analysis of three existing large data sets as a means to ask new questions and generate new nursing knowledge. Methods: Three research studies using the analysis of three existing large data sets were described.

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https://online.shrs.pitt.edu/blog/data-analytics-in-health-care/

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.

What is health care data?

In order to discuss health data analytics and the role it plays in the health care sector, we must first understand the data that is being collected and analyzed. There is data being collected on the processes and procedures of the business side of health care, but there is also an enormous amount of health data being gathered, stored and analyzed.

Health data is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.

We are able to see a holistic view of each individual patient as well as trends tied to location, socioeconomic status, race and predisposition. The information being collected can be broken down into specific datasets that can then be analyzed.

There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:

Electronic Health Records (EHRs)

Personal Health Records (PHRs)

Electronic Prescription Services (E-prescribing)

Patient Portals

Master Patient Indexes (MPI)

Health-Related Smart Phone Apps and more

With digital data collection, there is more and more health care data to be analyzed every second. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real time.

These data sets are so complex that traditional processing software and storage options cannot be used. Cloud storage is a necessity when dealing with "Big Data." Cloud storage is built to be secure, an absolute must when dealing with sensitive patient information. It is also very cost-efficient and has been helpful in lowering the increasing cost of health care.

Big Data Analytics and COVID-19

The impact COVID-19 has had on the health care industry is evident to anyone and everyone. You don't need to be plugged into the world of medicine to see what has been happening worldwide during this pandemic.

What most people don't see, though, is the impact COVID-19 has had on health care data analytics. "Big data tools have played an increasingly significant role in health care decision-making" says HealthITAnalytics. It is not just providers, but lawmakers and researchers who are turning to big data analytics and predictive models to help allocate resources, predict surges, improve patient care and outcomes and employ preventive measures.

Big data and health data analytics have played an integral role in the fight against COVID-19. The data is coming in at a near constant rate. Analyzing that health data has allowed for a better understanding of how to respond and treat patients.

This pandemic has resulted in an enormous surge of health data being recorded and manipulated allowing for bigger and better analytics. Unfortunately, we are also seeing that COVID-19 is " shining a harsh spotlight on health care's biggest issues. " There are a lot of obstacles when it comes to sharing health data across organizations and a distinct lack of standardization in the way that data is collected and analyzed.

This widespread problem was evident in the early days of the pandemic as conflicting and ever-changing information was being presented to the public. We saw a turn towards disbelief when it came to COVID-related information with many still believing misinformation and previously held beliefs on how this virus should be handled.

The spotlight that COVID-19 shined on these problems, however, will allow for them to be rectified. The providers, researchers and policymakers can learn from these mistakes and work towards a better, more standardized solution for big data in health care.

The Importance of Health Care Data Analytics

We can collect all the data we want, but it doesn't do any good if we don't know what to do with that information. We need a centralized, systematic way of collecting, storing and analyzing data so we can use it to our advantage.

The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.

The fee-for-service style of health care is becoming a thing of the past. There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regards to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners are able to identify patients at high risk of developing chronic illnesses and help to treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations.

If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.

Consider the impact this has had on the COVID-19 pandemic. The data being collected is analyzed in real time to understand the effects of the virus better and predict future trends so we may slow the spread and prevent future outbreaks.

Types of Health Care Analytics

Not every question can be answered by using the same analysis of the data. Through the use of different types of big data analytics, we can answer many of the questions being asked in health care settings.

Descriptive Analytics

Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.

Predictive Analytics

Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.

Prescriptive Analytics

Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.

How Data Analytics Leads to Health Care Solutions

Health care data management has the potential to lead to better care if used properly. With centralized datasets, there is immediate access to necessary information whenever and wherever it is needed. The addition of big data analytics improves efficiency on all fronts. Better data leads to better care.

Predictive Modeling

Predictive modeling is the process of analyzing current and historical data to predict future outcomes. Models use data mining, machine learning and statistics to identify patterns and predict outcomes. Predictive models built off of the health data being collected provide solutions on the macro and micro level.

The use of predictive analytics can alert health care professionals to potential risks. By analyzing behavioral data, we can predict treatment outcomes, potential risks for chronic illness and even predict risk of self-harm. The health data collected can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level.

Predictive modeling can also be used on a much larger scale. Population health management is impossible without the use of these models. Outbreaks can be predicted, outcomes can be predicted, and in knowing what is to come, preventive measures can be taken.

Predictive modeling can even be used in administrative applications to increase efficiency and lower costs for all.

Reductions in Health Care Costs

Health care is expensive. And, those costs only continue to increase across the board. We are, however, seeing a shift from fee-for-service payment models to value-based care.

Through the use of predictive and prescriptive analytics, health care organizations and practitioners can get detailed models for lowering costs and patient risk. In addition to the patient-centric benefits mentioned above, health data analytics can reduce appointment no-shows, manage supply chain costs, prevent equipment breakdowns and decrease fraud.

The Growing Role of the Health Data Analyst

Health Data Analysts take the health care data being collected and use skills such as data acquisition, data management, data analysis and data interpretation to provide actionable insights. The role of big data in health care and the increasing need for improvement in the health care sector has led to a higher demand for qualified health data analysts.

The role of a health data analyst varies based on their position and industry of choice. Regardless of industry, a health data analyst will need to be able to work with, develop and evaluate health information technology (health IT) and other health information systems (HIS).

They may also be expected to:

Collect or mine data

Examine current and historical data

Evaluate raw data

Build predictive models

Automate reports

Required Skills for Health Data Analysts

Health data analysts solve problems for the organizations that hire them. In order to do so, they should be well versed in several essential skills:

Structured Query Language (SQL)

Excel

Statistical Programming

Data Visualization

You should also have certain soft skills as a health data analyst such as:

Analytical thinking

Creative thinking

Attention to detail

Strong communication skills

Schooling for Health Data Analysts

While it is not a requirement to become a health data analyst, many jobs will require a bachelor's degree in health information management (HIM) or a related field (data science, mathematics, health informatics, statistics, etc.).

A master's degree is, again, not a requirement to work as a health care data analyst, but it will qualify you for higher, more competitive positions. You can earn your master's in data analytics, health informatics, or another related field.

Where Will A Health Data Analyst Be Employed?

Numerous industries and organizations utilize health data and require the help of health data analysts. These include:

Hospitals, private or public

Government health care departments

Diagnostic centers

Health insurance companies

Health care consulting companies

Large medical practices

Health IT vendors

Other health organizations

Depending on the industry or role they choose to take, health care data analysts may work as part of larger teams or alone.

How Much Do Health Data Analysts Make?

Your salary, much like your role responsibilities, will vary based on the industry or organization for which you choose to work. Payscale lists the median salary for a health data analyst as $63,000 per year. The U.S. Bureau of Labor Statistics doesn't have an entry specific to Health Data Analysts, however, the Medical and Health Services Manager page does list "health information manager." The median pay listed here is closer to $100,000.

Take the Next Step in Your Career

The University of Pittsburgh School of Health and Rehabilitation Sciences offers a Master of Science in Health Informatics degree with a Data Science track. If you want to enter the field of health informatics or health data analytics or open up your career options, this master's degree can make you a competitive candidate.

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The Role of Data Analytics in Health Care

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights and support decision-making at both the patient and business level.

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There are 4 types of data analysis in healthcare:  descriptive, diagnostic, predictive and prescriptive.

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https://synapse.koreamed.org/articles/1075868

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Big data analysis is receiving increasing attention in many industries, including healthcare. Visualization plays an important role not only in intuitively showing the results of data analysis but also in the whole process of collecting, cleaning, analyzing, and sharing data. This paper presents a p…

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4 characteristics of data visualization =Good Data Visualization

Accurate: The visualization should accurately represent the data and its trends.

Clear: Your visualization should be easy to understand.

Empowering: The reader should know what action to take after viewing your visualization.

Succinct: Your message shouldn't take long to resonate.

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647241/

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https://journal.ahima.org/page/data-visualization-an-essential-skill-for-health-information-management-professionals-1

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"A picture is worth a thousand words." The psychological basis of this well-known saying is that most people process and recognize patterns in visuals more quickly than from text signals. Today in the digital era, we are faced with ever-growing mounds of data-the meaning of which can be difficult to parse. Indeed, one could say that a picture is worth a thousand data points. The use of visual aids allows most people to understand and recognize patterns within large amounts of data much faster than when faced with the raw data alone.

In the digital era, data are generated and collected at an astronomical speed. Consequently, it has become increasingly challenging to make sense of a large, unwieldy amount of data. Healthcare is no exception. Healthcare data are collected and generated from a myriad of sources-electronic health records (EHRs), claims database, disease registries, imaging, sensors, genomics, patient-reported data, and so on. For example, it was estimated that, by 2020, there would be 2,314 exabytes (1 exabyte equals a million terabytes) of data in healthcare. Although emerging technologies such as big data, machine learning, and artificial intelligence could help store and process the data, individuals still need to communicate the findings from the data to stakeholders, customers, and colleagues. Therefore, data visualization, simply defined as the graphical representation of data and information, has become critically important for identifying and communicating the meanings of the data.

Data visualization has a long history in healthcare. Two prominent historical examples are data maps depicted by epidemiologist John Snow to identify and remove a water pump, the source of the cholera outbreak in London in 1854, and the Rose Chart (Coxcomb chart) drawn by Florence Nightingale to communicate the avoidable military mortalities during the Crimean War (1853-1856). Both examples illustrate the representation of data visually being vital to pattern recognition and decision-making.

Nowadays, with the help of computer software, data can be visualized with the ease of just a few mouse clicks. Unfortunately, it is also easier to generate misleading, incomplete, cluttered, and hideous data visualizations that often hamper the decision-making process. For this reason, it becomes essential for a manager to possess the mindsets and skills to create accurate and effective visualizations.

Recognizing the importance of visualizing data, the Commission on Accreditation for Health Informatics and Information Management (CAHIIM) stipulates data visualization as one of the curricular requirements at all three educational levels. Therefore, we expect the CAHIIM-accredited programs to implement data visualization content in their curricula to prepare the next generation of health information management professionals.

Associate "III.4 Report health care data through graphical representations."

Baccalaureate "III.4. Examine health care findings with data visualizations."

Master "III.3. Present data visually through a computerized application."

Selected Principles of Effective Data Visualization

Data visualization is a broad field, drawing content from psychology, statistics, computer science, cartography, art, etc. While it is impossible to discuss all tenets of the field in one article, six principles essential for building effective and accurate data visualizations are highlighted below. The first three principles are general, while the last three are more specific to creating charts. Although data could be visualized in various formats, this article's main focus is on charting-representing data using graphs such as bar or line charts. Anyone interested in learning more about data visualization could and should find ample excellent resources-books, websites, blogs-by searching the web. It is imperative to point out that even though data visualization could be used to clean data, such as identifying outliers and errors, this article assumes that the data have been pre-processed and cleaned from the visualization.

1. Understand the Needs of Your Audience

Understanding the needs of your audience is vital to effective data visualization. To effectively convey a message, you must understand your audience from more than one perspective. Questions you need to ask before preparing the data visualization include:

What is the purpose of the data visualization? Specifically, is it for data exploration or explanation? Must any important information be included? Information collected from these questions often sets up the design direction of the data visualization-static vs. dynamic, highlights vs. plain design, etc.

What are the roles of the readers of the data visualization? If you present data to a seasoned chief financial officer with a sound accounting background, include a table of original data. Conversely, if the busy chief executive officer is in your audience, strategic information, such as that related to the market and competitors, must be included in the data visualization to facilitate the CEO's decision-making.

How much time do they have to understand the visualization? If they have ample time to process the data visualization, you can create a complicated one with multiple layers of data to examine over time. Otherwise, create multiple simple visualizations to collectively convey the same information but take less time to process the individual ones.

What are their technical skills? For example, a user may like a still image hard copy printout better if they are not familiar with the underlying data or the software tool. On the contrary, a user may desire to access a dashboard with filter choices to explore the data by themselves.

What are some of the constraints the users may have? Without knowing your audience beforehand, you should always choose design that is friendly to everyone, including people with disabilities. For example, colorblind users will not be able to discern certain combination of colors that are used for coding data, so you should avoid using those colors in your design.

2. Create Data Visualization with Integrity

Data visualization is a process of gaining information from raw data. In this sense, it is not different from statistics. The impact of misleading data visualization could be even worse than that of distorted statistics because of the long-lasting impression of visual signals. For example, the default setting of Microsoft Excel when creating a bar chart is not to start the Y-axis at 0, which results in the distortion of inter-bar comparison by artificially magnifying the differences between bars. Another common misrepresentation of the data is to create a pie chart using a list of numbers in percentages, while the sum of all these percentages is not 100 percent. A bar chart should be used in this situation.

Data visualizers should always be mindful of the context of the data at hand. Missing critical contextual information will lead to inaccurate and incomplete data visualizations. For example, mortality rates due to substance misuse might have decreased in one country over the past five years. However, before celebrating the achievement, a comparison to the norm, such as the same five-year trend of average mortality rates due to substance misuse at the state level, needs to be include for a "complete picture."

The final product of the data visualization must be complete, objective, and without perceptual distortions. A detailed analysis plan is a tool to ensure the integrity of the visualization. Pilot testing the draft data visualization with potential users will also help in identifying flaws in the design.

3. Maximize the Data/Ink Ratio

Data/ink ratio is a concept introduced and advocated by the influential data visualization expert Edward Turfe. ¹ It reflects a minimalist design of data visualization. Although not everyone may favor the principle, it helps ensure that the visualization remains uncluttered to make important information prominent. Data-ink is the "non-erasable ink" used for the presentation of data. The non-data ink is the ink or components used in a chart that contribute nothing to the presentation of data. The concept is illustrated in the image below, and the goal of data visualization is to get the data-ink ratio closer to 1.0.

1.0 - the proportion of a graphic that can be erased

Based on the principle of maximizing data-ink ratio, the following elements of a chart could be reduced if they don't contribute to the representation of the data: 3D effects, background images, shadow effects, unnecessary borders, and overused grid lines. Unfortunately, many of these non-data-ink elements are in the default setting of charts created by commercial software. So, extra efforts will be needed to remove the non-data ink from the charts created by these software tools.

4. Select the Proper Chart Based on the Data and Functions of the Chart

New users often want to know what the best tool is to visualize data. There are many different software tools available now, including Microsoft Excel, Tableau, SAS, R, and D3, to name a few. Each of these tools has its strengths, weaknesses, and learning curves. What is more important is to choose the proper chart for the data you like to visualize. The selection process should be based on functions of the chart (comparison, relationship, composition, distribution, mapping, etc.) and types of data (nominal, categorical, ratio, interval, qualitative, etc.). The Data Visualization Catalog hosts a list of charts searchable based on the functions of the charts and data types. Andrew Abela from the website Extreme Presentation developed a one-page cheat sheet to help users choose the proper charts. For Microsoft Excel users, the website Juice Analytics implemented a chart chooser specifically for Excel users with sample implementations in Excel.

5. Use Pie Charts Judiciously

A pie chart is an excellent choice to represent a part/whole relationship of a categorical variable. For example, the racial composition of a patient panel is a good candidate for a pie chart. The circle represents all possible values of the categorical variable, while the slices represent the percentages of the observations of each category comparing to the total counts. One fundamental flaw of the pie chart is that it is hard to discern the difference between two slices if their values are similar. A bar chart should be used in this situation. Another common challenge of using the pie chart is having a separate chart legend, which forces users to scan back and forth between the chart and the legend to make the mental connection. If you have to use a pie chart, use it when there are no more than three large slices and, when possible, have the labels adjacent to or in the slices so users can make the connection quickly.

6. Avoid Using 3D Charts

Many people like to create a data visualization in 3D because it looks "cool." Some even use the additional third dimension to represent one additional variable in data visualization. However, the fundamental drawback of a 3D chart is the perceptual distortion of the values associated with the 3D elements. A reader of the 3D chart can no longer precisely discern the difference between the elements of the chart. Therefore, they may not be able to make the right decision based on the charts. Instead, whenever possible, always represent data in a 2D format. The additional variables could be added to the 2D charts using color, size, or annotations. Since we rarely need to print out data visualization anymore, we can continually create multiple 2D charts along with the values of the additional variable and plot them out as a grid together to show the whole story.

The Era of Big Data

In summary, data visualization is a must-have skill for health information management professionals in the era of big data in healthcare. With the wide availability of computer tools, data can be easily visualized. However, data visualizers need to be mindful of its principles and strive for effective data visualization. Any deviation from these principles needs to be examined and well-justified. Effective and engaging data visualization will, in consequence, improve data-driven communication and decision-making.

Notes

Tufte, Edward R. The Visual Display of Quantitative Information. Cheshire, Conn. Graphics Press, 2001.
Xiaoming Zeng (xiaoming_zeng@med.unc.edu) is a research professor in the Department of Psychiatry at the University of North Carolina at Chapel Hill.
By Xiaoming Zeng, MD, PhD

# data[].attributes.attachment_link.title
Data Visualization: An Essential Skill for Health Information Management Professionals

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"A picture is worth a thousand words." The psychological basis of this well-known saying is that most people process and recognize patterns in visuals more quickly than from text signals. Today in the digital era, we are faced with ever-growing mounds of data-the meaning of which can be difficult to parse.

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928924/

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Abstract

Objective

We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors.

Materials and Methods

We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities.

Results

Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media.

Discussion

Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19.

Conclusions

The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Keywords: public health informatics, coronavirus, pandemics, health information interoperability, health information exchange

BACKGROUND AND SIGNIFICANCE

Coronavirus disease 2019 (COVID-19), caused by the novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus, is a global pandemic that, as of November 12, 2020, had affected more than 52.6 million individuals and caused over 1.2 million deaths. 1 In the United States, COVID-19 rapidly spread across large cities as well as across many rural areas. In Indiana, which ranks 17 among U.S. states by population with just under 6.7 million residents, COVID-19 challenged but did not overwhelm the health system. As of November 5, 2020, there were 196 176 confirmed cases with an estimated population-level prevalence between 3% and 15%. 2,3

Public health (PH) agencies play a critical role in managing disease epidemics. In the United States, responsibility for health rests with each state's governor, and therefore the state-level PH agency seeks to prevent disease, protect health, and promote well-being. Local and state-level PH agencies conduct disease surveillance, manage contact tracing, facilitate resource distribution including personal protective equipment (PPE), establish alternative care sites, coordinate investigation of long-term care facilities, and provide diagnostic support through laboratory testing. Federal PH agencies, such as the U.S. Centers for Disease Control and Prevention (CDC), support state-level efforts and coordinate response across states. 4 During a pandemic, PH and healthcare systems work together to identify and manage disease cases while working to prevent disease spread.

To manage disease epidemics, PH agencies utilize a variety of information systems, such as laboratory information management systems and case management systems. 5-7 Although existing information systems enable capture of data on laboratory-confirmed cases of COVID-19, the pandemic illuminated deficits in the U.S. information infrastructure. 8,9 The holes in the "swiss cheese" of PH infrastructures are the topic of media articles 10-13 because public demand for information quickly outstripped the capacity of most PH agencies to produce data on the emerging outbreak. For example, PH agencies do not typically receive information on hospitalizations until weeks postdischarge, when coding of the encounter has been finalized. 14 That is insufficient for pandemics, during which PH agencies need real-time situational awareness of health threats.

OBJECTIVE

Leveraging data available through a statewide health information exchange (HIE), we sought to support PH surveillance and response to COVID-19 through the development and implementation of novel visualization applications. Whereas other applications either present data for a single healthcare facility 15 or present aggregate counts of cases, our applications integrate clinical and PH data allowing for detailed examination of subpopulations. Our applications illustrate the role of informatics in PH, and their success is owed to (1) a robust health information infrastructure 16; (2) strong collaboration across the health system, including PH organizations; and (3) expertise in biomedical informatics.

MATERIALS AND METHODS

The visualization applications were possible due to a robust informatics environment in Indiana. The state possesses a mature, statewide HIE and a leading biomedical informatics research organization. Both entities routinely engage the broader biomedical ecosystem in the state (eg, industry, government, academic) in collaborations to address clinical, PH, and research challenges. Before we describe the visualization applications, we discuss the unique strengths of Indiana that made the rapid development and implementation of the applications possible.

A statewide information infrastructure

The applications leverage the Indiana Network for Patient Care (INPC), one of the oldest and largest health information networks in the United States. 17 The INPC is a repository of clinical and administrative health data collected from over 38 health systems representing 117 hospitals, 18 486 physician practices, commercial laboratories, and PH departments across the state. The INPC serves the clinical needs of providers, and it supports PH surveillance and research needs. 18-20 Support for PH and research is unique among HIEs in the United States. 21

Prior to the pandemic, the INPC possessed several hundred electronic feeds routinely capturing data from Indiana's health system. 22 Admission, discharge, and transfer (ADT) feeds provide real-time information on patients as they traverse the health system. Electronic laboratory report (ELR) messages are a required data feed for INPC members, allowing clinicians to access a patient's recent lab values and PH agencies to receive notifiable disease alerts via the Notifiable Condition Detector. 23,24 Encounter records are also provided by hospitals and clinics with diagnostic codes (eg, International Classification of Diseases-Tenth Revision-Clinical Modification) to enable review of problems and comorbidities. These data are exchanged predominantly using HL7 (Health Level 7) version 2.5.1 messages, although some source systems use alternative message formats including prior versions of HL7. The ADT and ELR messages further utilize LOINC as well as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), where available from the source.

In response to the pandemic, the Indiana HIE (IHIE) coordinated enhancements to the INPC with the Regenstrief Institute. Even before hospital and commercial laboratories began processing COVID-19 tests, the IHIE updated the Notifiable Condition Detector to recognize LOINC codes for COVID-19, leveraging the prereleased terms published on the LOINC website. 25 This positioned the INPC to automatically identify COVID-19 test results and route them to PH authorities once hospital and commercial labs began performing approved tests. The IHIE further incorporated additional data from health systems and hospitals not part of the INPC. Data from these facilities are processed into the INPC using ADT and ELR feeds like other institutions, enabling their retrieval for pandemic purposes seamless in conjunction with the usual INPC data. Similarly, the Indiana State Department of Health and Family Social Services Administration provide daily feeds of laboratory test results from multiple statewide locations offering COVID-19 specific testing for symptomatic patients as well as healthcare workers and institutionalized patients (eg, prisoner, nursing homes). Death records from state vital records are captured by the IHIE through the Michiana Health Information Network, which recently merged with the IHIE.

Biomedical informatics expertise and health data partnerships

Multiple organizations collaborated to design and implement the novel visualization applications. The collaboration was led by the Regenstrief Institute, a support organization to the Indiana University (IU) School of Medicine, and an internationally recognized informatics and health services research organization that is home to the Clem McDonald Center for Biomedical Informatics. The Center is also home to a collaborative program in Public and Population Health Informatics between Regenstrief and the IU Fairbanks School of Public Health. 26 The program team further partnered with the IHIE and state as well as county PH organizations to inform the design and use of the visualization applications, including Indiana State Department of Health, the Office of the Governor, Marion County Public Health Department, and Family Social Services Administration (which houses Indiana's Medicaid program).

Prior to the pandemic, Regenstrief collaborated closely with many of the partners on a variety of healthcare initiatives. Regenstrief and the IHIE have the closest relationship, as the IHIE was created by Regenstrief in 2004 to allow the HIE to scale operations across Indiana. The INPC began as a biomedical informatics research project. 27,28 Regenstrief now serves as the research and development partner to the IHIE, serving also as the honest data broker for the INPC. The PH informatics program at Regenstrief further meets regularly with PH agencies to discuss their informatics needs, and it routinely collaborates on CDC-funded projects to enhance the PH infrastructure in Indiana. Therefore, when the pandemic began, Regenstrief's biomedical informatics experts were already in routine contact with PH leaders and aware of what the major PH challenges would be during the unfolding disease outbreak across the state.

The Regenstrief COVID-19 Dashboard

To support PH response, we developed the Regenstrief COVID-19 Dashboard, a Tableau-based visualization application that collates information on individuals tested for and infected with COVID-19 in parallel with data on individuals with COVID-like illnesses, including influenza and pneumonia. The dashboard harmonizes data captured from multiple sources to inform situational awareness with respect to COVID-related testing, cases, hospitalizations, emergency department visits, intensive care utilization, mortality, recovery, comorbidity, demographics, and geography.

The dashboard is designed to visualize data relevant to the 3 components of PH surveillance: person, place, and time. 29 Persons are identified based on ELR message feeds and International Classification of Diseases-based diagnostic codes from emergency department and inpatient encounters. We employ probabilistic algorithms to uniquely identify each person across data sources to ensure accurate reporting of information because many persons receive multiple laboratory tests for COVID-19 due to (1) uncertainty in test results, (2) hospitals often test persons transferred from other hospitals and nursing homes, and (3) employers testing persons multiple times to determine eligibility to report for work. Place is based on the person's home address. All addresses in the INPC are geocoded. 30,31 Other data sources used by the dashboard report the person's home zip code and county of residence. Multiple dates are used to examine time. Persons appear in the dashboard when they either (1) receive a laboratory test for COVID-19 or influenza typically using the collection date for the test specimen or (2) receive a clinical diagnosis of COVID-19 using the date of encounter.

The specific data elements and their corresponding PH surveillance dimension are summarized in . The table also includes the source of each data element.

Analysts from Regenstrief's Data Services team query data from the IHIE once per day and transform them into an analytical dataset for the dashboard. The daily process (24/7) of querying involves not only data extraction, linkage, and deduplication functionality, but also descriptive statistical calculations. The rate per 100 000 population is calculated by dividing the number of individuals meeting numerator criteria by the 2018 Census-based population of the geographic area selected by the user then multiplied by 100 000. Overall prevalence positivity is calculated by dividing the number of total unique individuals testing positive by the total number of unique individuals tested. Daily incidence positivity is calculated by dividing the number of total unique individuals testing positive per day by the total number of unique individuals tested per day. Both measures are important to PH stakeholders. Prevalence estimates the total disease burden on a population, whereas incidence measures the population of individuals newly infected. Incidence helps PH agencies identify current trends in disease spread.

The initial version of the Regenstrief COVID-19 Dashboard was developed primarily to guide PH decisions in the early weeks of the pandemic. This "private" dashboard was accessible only to authenticated users in state and local health departments in order to enable data delivery at a level of granularity not appropriate for public consumption due to potential privacy concerns. After a few weeks, we developed a "public" version of the dashboard for broader use by health system leaders and other stakeholder groups. Given the rapidly developing nature of the pandemic, our dashboard team established a cadence of daily stand-up meetings to provide continuous dialogue and decision making in concert with our partners. Representatives from the IHIE and PH partners were on these calls. They informed which data would be harmonized and displayed and they provided input and feedback on designs conceived by the informatics team.

Both dashboards were developed using Tableau Desktop and are published each day using Tableau Server within the IU Information Technology Services environment. The project was approved by the institutional review board at Indiana University.

RESULTS

We launched the first version of the Regenstrief COVID-19 Dashboard on March 23, 2020. The dashboard debuted with a series of bar charts reporting individuals tested for COVID-19 or influenza as depicted in . Demographics for each person were displayed using stacked bar charts, and line graphs depicted cumulative hospitalizations since the start of the outbreak. Users could filter results by selecting a geographic area of interest (eg, county, zip code) or use a radio button at the top to filter results to a specific diagnosis or laboratory test of interest. Users could also change the time period of interest, allowing examination of data before or after certain events, such as the governor's stay-at-home order or the Fourth of July. These filters require recalculation of the descriptive statistics.

Enhancement of functionality and visualizations

Over the next 10 weeks, as cases and hospitalizations in Indiana continued to grow, we enhanced the functionality and information representations available in the dashboard. These enhancements were triggered by daily conversations with our PH partners as well as our internal experts, both groups desired new information or views of information to examine the unfolding outbreak. We further engaged business intelligence staff at the University Information Technology Services to support enhancement of the visualizations.

New functions and visualizations included:

Positivity: Early on PH partners suggested that, given delays between sample collection and test result availability, we should use COVID-19 test positivity in addition to the count of individuals reported as positive each day. Positivity normalizes reported infections as it divides the number of positive tests by the total number of tests performed. As testing capacity increases, positivity decreases when the same number of new cases are detected. PH officials desired to monitor testing capacity at state and county levels as well as in specific zip codes.

Estimated Recovery Metric: We also developed a metric to monitor the proportion of the population recovered from COVID-19. Our metric uses a 21-day window following a positive lab result. If an individual is not hospitalized, they likely have mild symptoms and is assumed to have recovered after 21 days. We also add to this population COVID-19-positive individuals who were hospitalized but have since been discharged for at least 1 week. We then subtract out from this metric the number of deaths among COVID-19 positive individuals.

Length of Stay: Using discharge dates as well as the difference in days between date of admission and today's date (for patients still hospitalized), we calculate hospitalization length of stay. These metrics help PH officials and epidemiology modelers monitor and forecast impact of the disease on the health system given concerns about overcrowding.

Population Pyramids: Originally, the dashboard featured stacked bar charts to track the distribution of COVID-19 among various populations based on age, race, and gender. These visualizations were helpful, but they became difficult to interpret as time progressed (especially after 2 weeks elapsed since we displayed daily bar counts). We shifted these visualizations to population pyramid charts to better represent the proportions of tested and hospitalized individuals by sex and race.

The evolving dashboard supported daily PH decision making, enabling measurement of key indicators like positivity and recovery. Enhanced information visualizations significantly changed the user experience, enabling novice users to more quickly interpret trends and proportions. An enhanced version of the private dashboard is depicted in . Although not all of the enhancements are visible in the figure, the radio buttons that allow for filtering are depicted along with headline numbers that provide users with a snapshot of the cumulative impact of COVID-19 on the state population.

Making the dashboard publicly accessible

In addition to enhancing the dashboard's functionality and visualizations, which are accessible behind the university's firewall to authorized users, including Regenstrief personnel and faculty as well as state and county PH staff, we also created a public version depicted in . In consultation with state PH officials, we identified information on the dashboard that would be useful to the public as well as health system leaders who could not access the private tool. Furthermore, the dashboard's measurement of hospitalizations was selected as a key metric for the governor's "Back on Track" plan 32 for reopening the state following an executive stay-at-home order in March 23, 2020. Regenstrief's public dashboard, therefore, would complement the state's official website by augmenting available COVID-19 data with information on hospitalizations, comorbidities, and recovery.

To make the information public, we constrained the functionality to maintain confidentiality as follows. First, we implemented a rule to hide metrics when a cohort size was less than 5 people. While counts for lab tests and healthcare utilization as well as death can be reported when smaller than 5, information regarding demographics and medical history (eg, comorbidities) is censored. Second, we only include information publicly on individuals who test positive for COVID-19, as it is a reportable condition (eg, influenza in the tracker can only be viewed by PH business associates). Finally, we do not allow users of the public dashboard to filter the results using the radio buttons. While these measures limit functionality, they maximize privacy.

Using the dashboard to identify local COVID-19 outbreaks

The dashboard identified several local outbreaks, including the one in a meat packing plant located in Cass County (). In early April 2020, individuals working at the Tyson Fresh Meats plant in Logansport, Indiana, began calling in sick. 33 By mid-April, the number of people testing positive in the community around the plant rose dramatically (). The orange line represents daily COVID-19 tests performed, and the red line represents the number of daily positive cases. Positivity for COVID-19 tests rose in parallel (), indicating an outbreak. By April, around 1 in 4 tests came back positive for county residents, many of whom worked at the plant. The plant temporarily closed on April 20, 2020, for cleaning and sanitizing. 34 Health officials also began testing all employees. On April 25, 2020, the plant closed for 2 full weeks due to growing numbers of employees testing positive. 35 While the health department would not disclose the number of employees who tested positive, county-wide data from the dashboard reveals 1558 (38% of those tested) individuals were positive for COVID-19 between early April and mid-May (). This equates to a rate of 3998 per 100 000 population, which is 1.5 times the rate for New York City and 8 times the national rate per 100 000 for COVID-19 infections. Hospitalizations were not immediate, but the epidemiologic data in show a rise following the plant closure until end of May 2020. The hospitalized population () skews older, whereas the infected population () skews younger, illustrating community-level impact of COVID-19. A total of 248 (15.9%) individuals were hospitalized and 28 (1.8%) died.

Monitoring localized COVID-19 trends

To support stakeholders (eg, health systems, community-based organizations) with identifying changing trends in their local area, we created a second page for the public dashboard to display recent patterns in COVID-19 metrics. This page identifies counties or demographic groups (eg, individuals 30-39 years of age) for which rates may be increasing or decreasing. This component of the dashboard, depicted in and released June 11, 2020, uses a stoplight color scheme to communicate the directionality of a given indicator. Red indicates an increasing trend in the most recent time period, and green indicates a decreasing trend during the most recent time period. Arrows are also used in the charts at the bottom of the page (not included in the figure) to help communicate direction, especially for those who may be colorblind.

Trends are calculated using a 3-element vector composed of the differences (represented as increase, decrease, or no change) between 4 consecutive 7-day averages. The trends identify regions or groups that may be experiencing changes in key COVID-19 metrics. Bar charts to the right of each map below show daily counts for each metric. If a notable increase in daily counts is observed in the most recent 3 days, a red dot is displayed. A "notable increase" is defined as any individual count in the most recent 3 days that exceeds the average of the preceding 7 days.

Dashboard usage

To measure usage, we queried data from the IU Tableau Server for the 8-week period of June 21 to August 22, 2020. This period occurred several weeks after most major updates to the public dashboard were completed and during a time when the dashboards were regularly cited in broadcast media stories and social media posts. Combined there were 156 008 pageviews, of which 133 637 (74.2%) were unique. These pageviews occurred among 74 317 users, of which 67 821 (91.2%) were new users. Just over half (50.5%) of users accessed the public dashboard via a mobile device.

Although users came from 9 distinct countries, nearly all (96.6%) were from the United States. Of the 71 990 users located in the United States, most (70.8%) accessed the site from Indiana. Yet individuals in neighboring states of Illinois (7.9%), Kentucky (2.3%), Michigan (1.7%), and Ohio (1.4%) were also users. These users likely work in Indiana or live near the Indiana border.

DISCUSSION

The private and public versions of the Regenstrief COVID-19 Dashboard are applications of informatics methods designed to support PH surveillance and response to the COVID-19 pandemic. Visualization of data, a shared area of research in data science and informatics disciplines, has long been an important aspect of PH surveillance. 36 Unlike past visualizations in the form of static reports published on a PH website, the dashboard represents a modern example of PH surveillance applications that offer interaction with dynamic visualized data, such as drilling down into county-level data or selecting a subpopulation. Interaction allows epidemiologists to explore trends and patterns of data more easily than traditional, more time-consuming approaches in which data are manipulated in spreadsheets. In a pandemic, this functionality enhances the ability to find patterns early, which can initiate PH investigation as well as response. Outbreaks of COVID-19 have not happened uniformly across the United States. Instead, outbreaks are highly localized events that require sophisticated applications for analysis of local data in near real time. The private dashboard helped PH officials identify trends and initiate response (eg, targeted testing in the community, shutting down impacted businesses) in Cass County, and it continues to help local and state health officers identify new clusters as well as shifting demographics among those impacted by COVID-19.

Neither dashboard would be possible without deep biomedical informatics expertise at Regenstrief, cross-sector partnerships, and the statewide HIE. The Faculty and Regenstrief Data Services team, already familiar with PH business processes, quickly pivoted their work to develop visualization applications to meet pandemic data needs. Together with PH leaders who regularly collaborate with Regenstrief faculty and staff, our response team defined then built the indicators necessary to track the spread of COVID-19. The operational HIE platforms in Indiana provided a solid foundation upon which the applications were quickly developed and deployed. Our close, unique relationship with the IHIE enabled Regenstrief to not only access the necessary data, but also expand available interfaces and connections to acquire new data streams. Staff at the IHIE further contributed ideas for analyzing and visualizing information, and they supported our efforts to secure, link, and present health information to PH authorities and the public. The role of our community-based HIE cannot be understated. Exchanges like the IHIE exist around the United States and globally, yet it is rare for HIE networks to support PH activities. 21

Rapid harmonization of data was possible given the broad adoption of standards and support for interoperability available in Indiana. Regenstrief is the home of LOINC, and the IHIE works closely with its members to adopt and use data standards. Adoption of standards remains a challenge for many HIE networks, and it remains a work in progress for Indiana. Prior work documented limited adoption of data standards by Indiana health systems. 37 Since then, the IHIE and Regenstrief worked to increase use of SNOMED CT and LOINC along with updated HL7 standards, including the C-CDA (Consolidated Clinical Document Architecture) 38 and FHIR (Fast Health Interoperable Resources). 39 When data are available in a clinical data network like INPC in a standardized format, retrieval becomes efficient and scalable. Efficiency is crucial during a pandemic when PH agencies desire rapid information exchange.

Where HIE infrastructures do not exist, states and nations should invest in HIE to support PH preparedness for pandemics while recognizing they further play a critical role in "peacetime" PH functions like chronic disease surveillance and immunization registries. 40,41 Indiana took full advantage of prior funding opportunities from the federal government, including demonstration grants from the Agency for Healthcare Research and Quality and CDC as well as HITECH (Health Information Technology for Economic and Clinical Health Act) funding from the Office of the National Coordinator for Health Information Technology to develop its statewide infrastructure. The IHIE further created a business strategy (eg, sustainability model) to maintain and enhance the infrastructure over time through various HIE services. This includes a variety of HIE services that meet PH information needs, including notifiable disease reporting 42 and syndromic surveillance. 43 In Indiana, PH as well as research are primary functions of HIE, not simply "secondary uses" that risk being deprioritized when budgets need to be cut. Following the pandemic, the United States and other nations should invest in developing robust HIE infrastructures to support clinical, PH, and research capabilities to prepare for emerging infections disease outbreaks while addressing every day population health needs in their community. This sentiment is echoed in a recent manuscript written by authors from 15 academic medical centers, including ours, 21 and it is embedded in the Public Health 3.0 framework described by DeSalvo et al. 44

Beyond increased investment in HIE infrastructure focused on PH, states need to work with regional HIE networks to ensure they are sustainable and create value for providers, patients, and communities. As national and state-level architectures are developed, HIE networks need to play key roles in population health activities. For example, Medicaid programs desire to address social determinants and upstream issues which require connecting social service agencies with clinical providers. HIE networks can and should offer services that create value for Medicaid programs and populations, including better care coordination following discharge 45 and improved access to wraparound services. 46 Over time, the IHIE developed new services on top of its robust infrastructure that create value for state agencies, providers, and patients, which sustains the core infrastructure while expanding to meet evolving community needs. Enabling this was a governance structure that includes representation (eg, on the IHIE Board of Directors) by PH and research leaders, alongside representative leaders from regional healthcare systems. As the Indiana experience also demonstrates, regional public and population health needs are unlikely to be fully addressed by EHR-based HIE services (eg, Commonwell, Carequality), which focus on retrieval of information for a single patient. By developing services that are multipurpose and responsive to local and state PH needs, regional HIE networks can add value to population health initiatives that meaningfully transform care delivery and outcomes.

An important limitation of any informatics application is the underlying data. Data quality challenges are widespread, and our efforts are no exception. In particular, we note that the full power of these tools cannot be realized given limited data on race and ethnicity. A sizable proportion of individuals tested for COVID-19 are missing race and ethnicity data, and EHR data were not available to backfill missing data in ELR messages. As noted in prior work, 47,48 health systems should improve their capture of race and ethnicity information. Improvements could include asking patients to self-report race and ethnicity directly into EHR or HIE systems using patient-facing tools.

CONCLUSION

Rapid development and deployment of PH informatics applications such as analytical dashboards during a pandemic requires a robust, interoperable health information infrastructure as well as strong cross-sector partnerships. Also critical is the ability to quickly engineer complex operational and infrastructural data processes to support mission critical PH data operations. These elements existed in Indiana, allowing the Regenstrief Institute, Indiana HIE, and PH agencies to quickly implement advanced visualization applications to monitor the spread and impact of COVID-19. Investment in infrastructure as well as rethinking the primary uses of HIE will be required to replicate Indiana's response for the next pandemic in other states and nations.

FUNDING

This work was supported by a grant from the Indiana State Department of Health to the Regenstrief Institute to support COVID-19 pandemic response. BED is further supported by grant number R21HS025502 from the U.S. Agency for Healthcare Research and Quality. The funders did not have a role in the writing or editing of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

AUTHOR CONTRIBUTIONS

The visualization applications were conceived and led by BED, PJE, and SJG. CM and AAB supported the data management needed to amalgamate the data, and CM developed the dashboards. UT facilitated the backend data services that support all aspects of the dashboards. AW, WM-C, and JLW coordinated the development and implementation of the dashboards along with the backend services necessary to operationalize them. BED drafted the manuscript. All authors contributed to revisions and approved the final version. All authors assume public responsibility for the accuracy and integrity of the work.

DATA AVAILABILITY STATEMENT

The data underlying the dashboards described in this paper originate from multiple sources, each of which has its own set of governance permitting access to third-parties. Publicly available datasets on COVID-19 indicators in Indiana represented on the dashboards can be downloaded from the Regenstrief Institute. Parties interested in these datasets should visit https://www.regenstrief.org/data-downloads/ to complete a request form and agree to the terms and conditions. They will then be permitted to download up-to-date data.

ACKNOWLEDGMENTS

We thank the Indiana University Decision Support Initiative, especially Mr Richard Shepherd, for their management of the Tableau infrastructure, troubleshooting technical challenges, and input on the visualizations. We further acknowledge the amazing support from the larger Regenstrief Data Services team whose many hours of extra effort are appreciated by the authors as well as the public health agencies in Indiana. Linking and receiving data from the state health agency was supported by the Management Performance Hub ( https://www.in.gov/mph/). Finally, we thank our public health agency partners for their rapid input on the applications and their many hours on the front lines in response to COVID-19.

CONFLICT OF INTEREST STATEMENT

The authors have no competing interests to declare.

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# data[].attributes.attachment_link.title
Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics

# data[].attributes.attachment_link.description
We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange.

# data[].attributes.body
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

# data[].attributes.body
https://humanfactors.jmir.org/2018/4/e11826/

# data[].attributes.attachment_link.readable_text
Original Paper

¹ Nursing and Healthcare Leadership, University of Washington Tacoma, Tacoma, WA, United States

² Biomedical Informatics & Medical Education, University of Washington School of Medicine, Seattle, WA, United States

³ Betty Irene Moore School of Nursing, University of California Davis, Sacramento, CA, United States

Corresponding Author:

Katherine K Kim, MBA, MPH, PhD

Betty Irene Moore School of Nursing

University of California Davis

2450 48th Street, Suite 2600

Sacramento, CA, 95817

United States

Phone: 1 510 761 5461

Email: kathykim@ucdavis.edu

Background: There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

Objective: The aim of the study was to assess the health care practitioners' perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.

Methods: Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners' rankings of overview visualizations.

Results: Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.

Conclusions: Visualizations guided by a framework and literature can support health care practitioners' understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

JMIR Hum Factors 2018;5(4):e11826

doi:10.2196/11826

Keywords

Background

The benefits of data visualizations-supporting understanding, task completion, and decision making-are especially critical in health-related settings such as cancer care facilities. In these settings, data are used to support important, critical, and time-restricted decisions that impact the health of individuals. Data visualizations are increasingly being incorporated into clinical care through integration of dashboards into health record systems. A recent review suggests that dashboards that integrate visualizations have the potential to support the cognitive work and decision making of intensive care unit clinicians []. However, there have been few examples of the effective use of person-generated data in personalized cancer care, particularly to enable shared decision making or care coordination []. A recent study found that patients with solid tumors who used a Web-based system to report symptoms experienced longer survival compared with usual care []. There is also little research on the value of visualizations within systems that integrate patient-generated data in cancer care. A pilot study conducted in Italy suggested that a dashboard that integrated remote monitoring and symptom-tracking data could be useful to HCPs and patients []. However, this work did not specifically evaluate the visualizations, and rationales for the visualization designs were not described. Therefore, a gap exists in the literature and practice regarding the development of informatics solutions that integrate and visualize person-generated data to support understanding and decision making regarding personalized cancer care among individuals with MCC.

Prior Work

OnPoint is a mobile app developed by the authors to support care coordination for individuals with MCC [,,]. Previous studies by the authors' research group that were conducted to support heart failure and oncology patients resulted in the development of a mobile app that featured patient health goals, proactive symptom assessment, comprehensive medication list and medication reconciliation, and tracking for patient and caregiver use. On the basis of this prior work, researchers identified the need for visualization of data collected from the app and integrated into the electronic health record for communication to HCPs. This inspired the study described below.

Study Purpose

The purpose of this study was to assess HCPs' perceptions of and feedback regarding visualizations developed to support the personalized care of individuals with MCC engaging in cancer care.

Design, Time Frame, and Setting

This user-centered study took place from May to June 2017 at a large, urban academic medical center in the western United States.

Recruitment of Participants

Visualization Development

Paper mock-ups of data visualizations were developed by UB (author) guided by Munzner Nested Model for Visualization Design []. We applied all model constructs (italicized in the following paragraphs) except for the algorithm design construct, which is suited for software development rather than for our focus on presoftware development.

The domain problem addressed was the need to support the care of individuals with MCC engaging in cancer care-the problem addressed by the OnPoint mobile app [,,].

For operation and data type abstraction, we identified operations (tasks) and data types from the previous studies [,,]. These data types were blood pressure (mm Hg), weight (kg), blood glucose levels (mg/dL), medication adherence (medications not taken at the time or frequency as prescribed), and symptoms from the Canadian Oncology Symptom Triage and Remote Support (COSTaRS) practice guides [,].

For visual encoding and interaction design, mock-up encodings and designs were guided by literature on (1) visualizing data [-,-]; (2) health data visualization [,-]; and (3) a mock patient persona and scenario [,]. The mock patient was a 56-year-old woman with uterine cancer and type 1 diabetes. She had recurrent work and personal constraints on Thursdays that interfered with taking medications as prescribed and managing her blood glucose levels. This patient also recently experienced weight gain due to fluid retention.

Measures and Procedures

In step 2, the researcher presented alternate versions of the 4-week overview (-). Ordering of the versions was varied from participant to participant so that the order in which the versions were presented did not influence responses. While viewing the alternate versions, participants described their overall impressions, aspects they perceived as helpful, and aspects they perceived as confusing. Then, the then researcher gave the clinicians the original 4-week overview () and asked participants to order this overview and the alternate versions from most helpful (ranked first) to least helpful (ranked fourth). Participants explained aloud their rationale for the ordering while sorting the versions.

In step 3, participants viewed the visualization that provided summaries of longitudinal patient-reported symptoms (). Again, the participants were asked to describe their overall impression of the visualization, aspects that they perceived as helpful in understanding the patient, and aspects they perceived as confusing in understanding the patient. At the end of the interview, the researchers asked whether and how the visualizations could help personalize the care and asked for suggestions.

Interviews were recorded, transcribed, and supplemented by field notes taken by researchers during the interviews. This study was approved by the affiliated institutional review board. All participants provided verbal consent after receiving and reading the study consent form. Participants were provided with a US $50 Amazon gift card for engaging in the interview.

Scenario used during one-on-one interviews with clinicians to elicit feedback about data visualizations. Information in brackets indicates actions by the researcher conducting the interview.

1. You have arrived at the clinic before you start seeing patients. You want to see how your first patient of the day is doing. Her name is Deb Lee (age 56 years). Three weeks ago she completed chemotherapy for uterine cancer. She also has type 1 diabetes that was diagnosed in childhood.

2. You have done your typical chart review of Deb's clinical data using the clinic's electronic health record. After that chart review of electronic health record data, you want to see how Deb is doing at home. Recently the clinic started supporting patients in collecting data at home. Data include:

if the patient took her medications as prescribed

blood pressure (measured twice a day)

weight (taken once a day)

blood glucose (taken periodically throughout the day using a traditional finger-prick monitor)

3. These data collected by patients are provided to you first as 4-week summary. [Participant given ]

4. You want to know what's going on with some of the data. You hover over several readings reading to get more information. After you hover, you get this visual. [Participant given ]

5. You are concerned about Deb's blood glucose readings and want to see more details about her readings over the 4 weeks. You click on the most recent reading to get more information. After you click, you get this visual. [Participant given ]

6. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot on "This week," Thursday at 12:00 pm]

7. After you hover over it, you get this visual. [Participant given ]

8. You are concerned about Deb's weight readings and want to see details about her readings. You click on the most recent reading to get more information. After you click, you get this visual. [Participant given ]

9. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot for weight on "This week," Thursday]

10. After you hover over it, you get this visual. [Participant given ]

Textbox 1. Scenario used during one-on-one interviews with clinicians to elicit feedback about data visualizations. Information in brackets indicates actions by the researcher conducting the interview.

Analyses

Participants

A total of 8 HCPs participated in the interviews. Out of these, 4 were MDs; 1 was a pain management specialist (participant MD1), and 3 were oncologists (participants MD2, MD3, and MD4), and 4 were RNs (participants RN1, RN2, RN3, and RN4). Each participant provided care to cancer patients in the cancer center. Interviews lasted for approximately 25 to 42 min.

Themes

We identified 7 themes. Of these, 2 themes were not directly relevant to the visualizations; therefore, for the purpose of this paper, we report the following 5 themes: data elements, supportive elements, confusing elements, interpretation, and use of visualization. See for descriptions and specific content regarding the themes.

Table 1. Themes identified from interviews with health care practitioners while evaluating visualizations to support cancer care of an individual with multiple chronic conditions.

Theme

Description

Specific content regarding the theme

Data elements

Existing or future or potential data elements (eg, weight, blood pressure, medication adherence, and symptoms)

data useful for specific role in cancer care included weight and medications (,, and ) and the list of symptoms () [MDa1 and RNb2]

suggestions for additional data elements or information included additional measures such as heart rate [MD1 and RN4], temperature [RN1 and RN2], body mass index [MD3], lab values [RN1], meal times or what eaten [RN1, RN2, MD3, and MD4], physical activity engagement [RN1], sleep [MD1], swelling [MD2], symptoms that may be particular or specific to certain cancer therapies [MD2, RN2, and MD3]

patient-identified symptoms [RN2 and MD3]

a legend defining the symptoms [MD1]

meaning of the symptom scale ratings [MD1]

reasons for missed medications [MD2, MD3, and RN1]

values and description of the normal values and ranges for blood glucose and blood pressure [MD2]

goals of care [RN1]

treatments [RN1]

patient-reported reasons for abnormal values [RN2]

‎

Supportive elements

Aspects of the visualization that supported the participant's understanding of the patient or that they thought were helpful

the color orange drew attention and helped participants find data points or patterns in the data that might require attention or indicate something abnormal more easily [MD1, MD3, MD4, RN1, RN3, and RN4]

calendar format and line graphs were helpful because clinicians are accustomed to them [MD1, RN1, RN2, and RN4], are used in practice [MD1 and RN1], and help see trends [MD1-MD4 and RN1-RN4]

having details on demand was helpful [MD1-MD4 and RN1-RN4] and does not to lead to overpopulation of data within the visualization [MD3]

suggestions for additional supportive elements included a pop-up with a numeric scale for normal ranges [MD4] or an indication of how the normal range was derived [MD3]

‎

Confusing elements

Aspects of the visualization that the participant does not understand or finds confusing or unhelpful

: Unsure if blank spaces indicated that measurements were normal or not taken [MD2, MD3, and MD4]

: Circles confusing or overwhelming [MD2, MD4, RN2, RN3, and RN4]

: Unsure if the empty rectangles indicated normal readings or no measures taken that day [RN2, RN3, RN4, and MD4]

: Lack of clarity about the meaning of the numeric scale [RN3 and RN4]; unsure about threshold values for the numeric scores that led to values being highlighted in orange or whether the thresholds were the same across all symptoms [RN1]; miniature line graphs hard to interpret [MD1, MD2, MD4, and RN3]; and hard to understand, compare, and interpret the 2 different ways of quantifying symptoms [RN1, RN2, and RN4]

‎

Interpretation

Information obtained or conclusions drawn about the patient from the visualization

Use of visualization

Ways the participant would or could use the visualization

visualizations could help clinicians gain an understanding of patient outside the clinic [MD1 and MD2], help them prepare specific questions regarding Thursdays [MD1-MD4, and RN4] and the cause of rapid weight gain [MD2], discuss the patient's symptom experiences or management [RN4 and MD4], make clinic time more efficient [MD1 and MD2]

visualizations could help patients remember health experiences [MD2] and empower patients to engage in health management [MD4]. Together clinicians and patients could use visualizations to support personalized cancer care [MD1-MD4, and RN1-RN4], facilitate interactions that focused more on the patient and their specific needs [MD1, MD2, RN2], and better guide conversations between clinicians and patients [RN3, RN4, MD3, and MD4]

‎

Data Elements

Can we plan another category that I want the patient to monitor? For example, if they're having bleeding-vaginal bleeding-can they use the category of vaginal bleeding to show me...?
[MD3]

Supportive Elements

Participants described several visual elements that supported their understanding of the patient. The elements included color, the calendar format, use of line graphs, and the ability to get "details on demand." All participants (n=8) stated that color supported their understanding of the data. Several indicated that icons helped differentiate graphs (n=4) and that the gray bands indicating normal ranges helped identify abnormal data points (n=4). Participants stated that the calendar format and line graphs were helpful because they are accustomed to them (n=4), are used in practice (n=2), and help them see trends (n=8):

[The line graph] gels with what practitioners could be used to...You don't want to have something too novel where you have some bizarre bar graph or some kind of odd, interesting pattern that's in 3D...that people haven't seen.
[MD1]

Three participants stated that the calendar format allowed them to see trends such as missed medications on Thursdays (RN1, RN2, and RN4):

...you see a pattern...that helps you identify that there is a regimen and that there's a schedule...it enables you to see something missed in the pattern by seeing the...[entire] month.
[RN4]

All participants (n=8) reacted positively to "details on demand" features such as hovering over a data point to get a pop-up with detailed information:

...it's good that it [the visualization] doesn't overpopulate the numbers right there and then because I mean I would just be overwhelmed with actual numbers, so this hovering thing is really good.
[MD3]

Confusing Elements

Several participants noted issues with interpreting visualizations for patient-reported symptoms (). This included lack of clarity about the meaning of the numeric scale, threshold values for the numeric scores that led to values being highlighted in orange, and whether the thresholds were the same across all symptoms (n=3). Participants also found it difficult to interpret the miniature line graphs (n=4) and to understand, compare, and interpret the 2 different ways of quantifying symptoms (n=3).

Interpretation

All participants (n=8) used visual elements to interpret data-finding patterns and viewing trends-to support understanding and decision making. They identified missed medications including the pattern of missed medications on Thursday using the calendar views (n=6). When seeing the pattern on Thursdays, participants were prompted to think about what could cause the patterns:

I wonder what's going on Thursdays that she always forgets the medications.
[MD3]

Identifying this pattern supported RN4's decision making to investigate the cause of the pattern:

I'm not sure why [she is missing her medications consecutively on Thursdays] so you would have to find out why is she missing her drugs on Thursday.
[RN4]

MD4 similarly described how using the visualizations supported understanding of the patient, reasoning about what might be causing abnormal readings, and ruling out potential causes:

...she just is not taking her medications for some reason...I can use the visualization and the colors to figure out some of her difficulties...[about] why she may not be adherent with her health [behaviors]and medications.
[MD4]

RN4 echoed how the visualizations could facilitate clinician reasoning, stating that a clinician could postulate about what might be causing the issues on Thursdays by bringing in symptoms and other data:

...you could really get I think a good picture.
[RN4]

Trends in line graphs helped participants interpret temporal glucose, blood pressure, and weight data (n=5). Participants reflected on several weeks' worth of data, comparing normal and abnormal points over time as well as visual elements indicating missed medication, to formulate whether they believe the patient had well-controlled blood glucose (n=2). For example, MD4 stated:

[if the medication being missed is] related to her insulin...and her blood sugars aren't controlled, [then] the general impression probably is that her blood sugars aren't controlled and her diabetes isn't controlled.
[MD4]

I also see that the same kind of pattern I've seen on this very day and the week before.
[MD3]

Use of Visualizations

Participants stated that visualizations could help gain an understanding of the patient outside the clinic and prepare specific questions to facilitate discussions with the patient about their self-management outside the clinic. This included discussing circumstances on Thursdays that made health management challenging (n=5), asking questions to help investigate the cause of rapid weight gain (n=1); and understanding symptom experiences or management (n=2). MD2 stated that the visualizations provide insights that "might open up a door to other questions that you normally wouldn't ask if you didn't [see trends]."

In addition, participants noted that visualizations could be helpful for patients. Having the visualizations during clinic visits could help patients remember symptoms; MD2 stated that the visualizations provide "another way to understand if the patient had any symptoms but forget to mention [them] to us or we forgot to ask [about them] during the clinic visit." Visualizations could also support patients feeling empowered; MD4 stated that a visualization tool could empower patients to engage in health behaviors such as taking medications and "be more aware of their symptoms" regularly.

All participants (n=8) mentioned the use of visualization to personalize visits with patients. Visualizations helped them identify issues specific to the patient that needed to be addressed, making interactions more focused on the patient and their specific needs (n=3). Visualizations could also give a clearer and focused picture of the patient, their health status, and needs that can better guide conversations and interactions with patients (n=4):

I think it would cause us to get a good picture, get a fast picture, evaluate that with the patient so we don't walk into an assumption, but dive a little bit quicker if we needed to.
[RN4]

MD3 stated that the visualization could personalize visits by bringing "attention to the important things" and focus "conversations with the patient directly to what's the issues or the problems that now I see [using the visualizations]..." RN4 stated that a benefit of the visualizations through personalizing visits with patients could be earlier identification of issues "rather than waiting until things [snowball]."

Ranking of Overview Visualizations

Participants varied in their preferences for the 4-week overviews. On average, participants ranked as most helpful (mean 1.8 [SD 1.2]) and least helpful (mean 3.9 [SD 0.4]; see ).

Principal Findings and Comparison With Prior Work

In our user-centered design study, we found that MD and RN participants' understanding of physiological and symptom data for individuals with MCC engaged in cancer care was supported by visualizations we developed by applying a visualization framework and relevant literature. Both MD and RN participants found that various visual encodings such as color, and familiar presentation such as calendar formats and line graphs supported their interpretation of the presented data. This research used foundational knowledge in data visualization in a novel way to develop visualizations that both MD and RN participants found helpful and effective in integrating various health-related data. Our MD and RN participants also noted the potential usefulness of the visualization in supporting personalized care. This user-centered design study offers findings from potential clinician users of the output of patient-generated health data from the OnPoint app. These results will be used specifically to inform the integration of visualizations into OnPoint system in the next phase of the project.

Although we cannot assume that an interactive tool incorporating visualizations for use by individuals with MCC engaged in cancer care would necessitate the same design as a tool for clinicians, we do believe that this study offers a starting point for features to consider for users who are patients.

Our study findings are congruent with guidance and best practices described in the visualization literature. Both MD and RN participants noted that color helped them see patterns in the data or pick out data that require attention, congruent with work described by Ware []. They also were able to use the line graphs to identify meaningful patterns in the data; this aligns with recommendations based on work by Cleveland and McGill [-] and Mackinlay []. In particular, position rather than other data encodings (eg, area) supports more accurate interpretation of the data being represented by the encoding. Although we did not compare our line graphs with other graph types in this study, participants responded positively to our design choice that was guided by the data visualization literature.

Implications for Developing Health-Related Visualizations

Finally, engaging potential end users early in the design ideation process was feasible and insightful. To minimize time and burden on HCP participants, researchers can carefully develop study protocols so they can maximize opportunities for participants to provide insights such as using mock patient personas and scenarios to guide eliciting feedback about mock-ups. Using personas and scenarios is advocated within the human-computer interaction domain [,], and it has been used to support the development of health informatics tools [,].

Limitations

There were limitations to our study. Our sample was limited to MDs and RNs; these visualizations could be useful to other HCPs supporting individuals with MCC engaging in cancer care, such as care coordinators, dieticians, pharmacists, and social workers. In addition, inputs from patients themselves and their family members must be collected to understand their informational needs. This study was conducted at a single cancer center; therefore, it has limited generalizability to other cancer centers. Mock-ups were on paper rather than on a device that a clinician would use to view visualizations in practice (eg, computer tablet and desktop computer). The data visualization literature used to guide the development of our mock-ups has not been tested extensively and empirically within health-related apps for cancer care; our work can support the building of evidence regarding the application of the visualization literature within this health domain. Although the visualizations are intended to be delivered via the electronic health record, we did not explicitly address how this might be accomplished. This work will be pursued in a future phase.

Conclusions

This study suggests that visualizations guided by a framework and literature can support HCPs' understanding of data to support personalized cancer care for individuals with MCC. By integrating health informatics and visualization literature and applying user-centered design methods, we were able to develop and elicit feedback on visualizations for health-related data including person-reported data. Future research could apply these methods toward the development of visualizations to support the care of other populations and the development of functional systems integrated into clinical and personal health care.

Acknowledgments

Funding for this study was provided in part by the Boston University-Center for the Future of Technology in Cancer Care (National Institute of Biomedical Imaging and Bioengineering award U54 CFTCC U54-EB015403-05) for authors UB, SCH, and KKK; and Gordon and Betty Moore Foundation grant to the Betty Irene Moore School of Nursing for SCH. The authors would like to thank Petra Backonja for her editorial support.

Conflicts of Interest

None declared.

‎

Edited by G Eysenbach; submitted 03.08.18; peer-reviewed by S Khairat, K Colorafi, T Cruvinel; comments to author 22.08.18; revised version received 29.08.18; accepted 08.09.18; published 16.10.18

©Uba Backonja, Sarah C Haynes, Katherine K Kim. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 16.10.2018.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on http://humanfactors.jmir.org, as well as this copyright and license information must be included.

Background: There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

Objective: The aim of the study was to assess the health care practitioners' perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.

Methods: Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners' rankings of overview visualizations.

Results: Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.

Conclusions: Visualizations guided by a framework and literature can support health care practitioners' understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

JMIR Hum Factors 2018;5(4):e11826

doi:10.2196/11826

Keywords

Background

The benefits of data visualizations-supporting understanding, task completion, and decision making-are especially critical in health-related settings such as cancer care facilities. In these settings, data are used to support important, critical, and time-restricted decisions that impact the health of individuals. Data visualizations are increasingly being incorporated into clinical care through integration of dashboards into health record systems. A recent review suggests that dashboards that integrate visualizations have the potential to support the cognitive work and decision making of intensive care unit clinicians []. However, there have been few examples of the effective use of person-generated data in personalized cancer care, particularly to enable shared decision making or care coordination []. A recent study found that patients with solid tumors who used a Web-based system to report symptoms experienced longer survival compared with usual care []. There is also little research on the value of visualizations within systems that integrate patient-generated data in cancer care. A pilot study conducted in Italy suggested that a dashboard that integrated remote monitoring and symptom-tracking data could be useful to HCPs and patients []. However, this work did not specifically evaluate the visualizations, and rationales for the visualization designs were not described. Therefore, a gap exists in the literature and practice regarding the development of informatics solutions that integrate and visualize person-generated data to support understanding and decision making regarding personalized cancer care among individuals with MCC.

Prior Work

OnPoint is a mobile app developed by the authors to support care coordination for individuals with MCC [,,]. Previous studies by the authors' research group that were conducted to support heart failure and oncology patients resulted in the development of a mobile app that featured patient health goals, proactive symptom assessment, comprehensive medication list and medication reconciliation, and tracking for patient and caregiver use. On the basis of this prior work, researchers identified the need for visualization of data collected from the app and integrated into the electronic health record for communication to HCPs. This inspired the study described below.

Study Purpose

The purpose of this study was to assess HCPs' perceptions of and feedback regarding visualizations developed to support the personalized care of individuals with MCC engaging in cancer care.

Design, Time Frame, and Setting

This user-centered study took place from May to June 2017 at a large, urban academic medical center in the western United States.

Recruitment of Participants

Visualization Development

Paper mock-ups of data visualizations were developed by UB (author) guided by Munzner Nested Model for Visualization Design []. We applied all model constructs (italicized in the following paragraphs) except for the algorithm design construct, which is suited for software development rather than for our focus on presoftware development.

The domain problem addressed was the need to support the care of individuals with MCC engaging in cancer care-the problem addressed by the OnPoint mobile app [,,].

For operation and data type abstraction, we identified operations (tasks) and data types from the previous studies [,,]. These data types were blood pressure (mm Hg), weight (kg), blood glucose levels (mg/dL), medication adherence (medications not taken at the time or frequency as prescribed), and symptoms from the Canadian Oncology Symptom Triage and Remote Support (COSTaRS) practice guides [,].

For visual encoding and interaction design, mock-up encodings and designs were guided by literature on (1) visualizing data [-,-]; (2) health data visualization [,-]; and (3) a mock patient persona and scenario [,]. The mock patient was a 56-year-old woman with uterine cancer and type 1 diabetes. She had recurrent work and personal constraints on Thursdays that interfered with taking medications as prescribed and managing her blood glucose levels. This patient also recently experienced weight gain due to fluid retention.

Measures and Procedures

In step 2, the researcher presented alternate versions of the 4-week overview (-). Ordering of the versions was varied from participant to participant so that the order in which the versions were presented did not influence responses. While viewing the alternate versions, participants described their overall impressions, aspects they perceived as helpful, and aspects they perceived as confusing. Then, the then researcher gave the clinicians the original 4-week overview () and asked participants to order this overview and the alternate versions from most helpful (ranked first) to least helpful (ranked fourth). Participants explained aloud their rationale for the ordering while sorting the versions.

In step 3, participants viewed the visualization that provided summaries of longitudinal patient-reported symptoms (). Again, the participants were asked to describe their overall impression of the visualization, aspects that they perceived as helpful in understanding the patient, and aspects they perceived as confusing in understanding the patient. At the end of the interview, the researchers asked whether and how the visualizations could help personalize the care and asked for suggestions.

Interviews were recorded, transcribed, and supplemented by field notes taken by researchers during the interviews. This study was approved by the affiliated institutional review board. All participants provided verbal consent after receiving and reading the study consent form. Participants were provided with a US $50 Amazon gift card for engaging in the interview.

Scenario used during one-on-one interviews with clinicians to elicit feedback about data visualizations. Information in brackets indicates actions by the researcher conducting the interview.

1. You have arrived at the clinic before you start seeing patients. You want to see how your first patient of the day is doing. Her name is Deb Lee (age 56 years). Three weeks ago she completed chemotherapy for uterine cancer. She also has type 1 diabetes that was diagnosed in childhood.

2. You have done your typical chart review of Deb's clinical data using the clinic's electronic health record. After that chart review of electronic health record data, you want to see how Deb is doing at home. Recently the clinic started supporting patients in collecting data at home. Data include:

if the patient took her medications as prescribed

blood pressure (measured twice a day)

weight (taken once a day)

blood glucose (taken periodically throughout the day using a traditional finger-prick monitor)

3. These data collected by patients are provided to you first as 4-week summary. [Participant given ]

4. You want to know what's going on with some of the data. You hover over several readings reading to get more information. After you hover, you get this visual. [Participant given ]

5. You are concerned about Deb's blood glucose readings and want to see more details about her readings over the 4 weeks. You click on the most recent reading to get more information. After you click, you get this visual. [Participant given ]

6. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot on "This week," Thursday at 12:00 pm]

7. After you hover over it, you get this visual. [Participant given ]

8. You are concerned about Deb's weight readings and want to see details about her readings. You click on the most recent reading to get more information. After you click, you get this visual. [Participant given ]

9. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot for weight on "This week," Thursday]

10. After you hover over it, you get this visual. [Participant given ]

Textbox 1. Scenario used during one-on-one interviews with clinicians to elicit feedback about data visualizations. Information in brackets indicates actions by the researcher conducting the interview.

Analyses

Participants

A total of 8 HCPs participated in the interviews. Out of these, 4 were MDs; 1 was a pain management specialist (participant MD1), and 3 were oncologists (participants MD2, MD3, and MD4), and 4 were RNs (participants RN1, RN2, RN3, and RN4). Each participant provided care to cancer patients in the cancer center. Interviews lasted for approximately 25 to 42 min.

Themes

We identified 7 themes. Of these, 2 themes were not directly relevant to the visualizations; therefore, for the purpose of this paper, we report the following 5 themes: data elements, supportive elements, confusing elements, interpretation, and use of visualization. See for descriptions and specific content regarding the themes.

Table 1. Themes identified from interviews with health care practitioners while evaluating visualizations to support cancer care of an individual with multiple chronic conditions.

Theme

Description

Specific content regarding the theme

Data elements

Existing or future or potential data elements (eg, weight, blood pressure, medication adherence, and symptoms)

data useful for specific role in cancer care included weight and medications (,, and ) and the list of symptoms () [MDa1 and RNb2]

suggestions for additional data elements or information included additional measures such as heart rate [MD1 and RN4], temperature [RN1 and RN2], body mass index [MD3], lab values [RN1], meal times or what eaten [RN1, RN2, MD3, and MD4], physical activity engagement [RN1], sleep [MD1], swelling [MD2], symptoms that may be particular or specific to certain cancer therapies [MD2, RN2, and MD3]

patient-identified symptoms [RN2 and MD3]

a legend defining the symptoms [MD1]

meaning of the symptom scale ratings [MD1]

reasons for missed medications [MD2, MD3, and RN1]

values and description of the normal values and ranges for blood glucose and blood pressure [MD2]

goals of care [RN1]

treatments [RN1]

patient-reported reasons for abnormal values [RN2]

‎

Supportive elements

Aspects of the visualization that supported the participant's understanding of the patient or that they thought were helpful

the color orange drew attention and helped participants find data points or patterns in the data that might require attention or indicate something abnormal more easily [MD1, MD3, MD4, RN1, RN3, and RN4]

calendar format and line graphs were helpful because clinicians are accustomed to them [MD1, RN1, RN2, and RN4], are used in practice [MD1 and RN1], and help see trends [MD1-MD4 and RN1-RN4]

having details on demand was helpful [MD1-MD4 and RN1-RN4] and does not to lead to overpopulation of data within the visualization [MD3]

suggestions for additional supportive elements included a pop-up with a numeric scale for normal ranges [MD4] or an indication of how the normal range was derived [MD3]

‎

Confusing elements

Aspects of the visualization that the participant does not understand or finds confusing or unhelpful

: Unsure if blank spaces indicated that measurements were normal or not taken [MD2, MD3, and MD4]

: Circles confusing or overwhelming [MD2, MD4, RN2, RN3, and RN4]

: Unsure if the empty rectangles indicated normal readings or no measures taken that day [RN2, RN3, RN4, and MD4]

: Lack of clarity about the meaning of the numeric scale [RN3 and RN4]; unsure about threshold values for the numeric scores that led to values being highlighted in orange or whether the thresholds were the same across all symptoms [RN1]; miniature line graphs hard to interpret [MD1, MD2, MD4, and RN3]; and hard to understand, compare, and interpret the 2 different ways of quantifying symptoms [RN1, RN2, and RN4]

‎

Interpretation

Information obtained or conclusions drawn about the patient from the visualization

Use of visualization

Ways the participant would or could use the visualization

visualizations could help clinicians gain an understanding of patient outside the clinic [MD1 and MD2], help them prepare specific questions regarding Thursdays [MD1-MD4, and RN4] and the cause of rapid weight gain [MD2], discuss the patient's symptom experiences or management [RN4 and MD4], make clinic time more efficient [MD1 and MD2]

visualizations could help patients remember health experiences [MD2] and empower patients to engage in health management [MD4]. Together clinicians and patients could use visualizations to support personalized cancer care [MD1-MD4, and RN1-RN4], facilitate interactions that focused more on the patient and their specific needs [MD1, MD2, RN2], and better guide conversations between clinicians and patients [RN3, RN4, MD3, and MD4]

‎

Data Elements

Can we plan another category that I want the patient to monitor? For example, if they're having bleeding-vaginal bleeding-can they use the category of vaginal bleeding to show me...?
[MD3]

Supportive Elements

Participants described several visual elements that supported their understanding of the patient. The elements included color, the calendar format, use of line graphs, and the ability to get "details on demand." All participants (n=8) stated that color supported their understanding of the data. Several indicated that icons helped differentiate graphs (n=4) and that the gray bands indicating normal ranges helped identify abnormal data points (n=4). Participants stated that the calendar format and line graphs were helpful because they are accustomed to them (n=4), are used in practice (n=2), and help them see trends (n=8):

[The line graph] gels with what practitioners could be used to...You don't want to have something too novel where you have some bizarre bar graph or some kind of odd, interesting pattern that's in 3D...that people haven't seen.
[MD1]

Three participants stated that the calendar format allowed them to see trends such as missed medications on Thursdays (RN1, RN2, and RN4):

...you see a pattern...that helps you identify that there is a regimen and that there's a schedule...it enables you to see something missed in the pattern by seeing the...[entire] month.
[RN4]

All participants (n=8) reacted positively to "details on demand" features such as hovering over a data point to get a pop-up with detailed information:

...it's good that it [the visualization] doesn't overpopulate the numbers right there and then because I mean I would just be overwhelmed with actual numbers, so this hovering thing is really good.
[MD3]

Confusing Elements

Several participants noted issues with interpreting visualizations for patient-reported symptoms (). This included lack of clarity about the meaning of the numeric scale, threshold values for the numeric scores that led to values being highlighted in orange, and whether the thresholds were the same across all symptoms (n=3). Participants also found it difficult to interpret the miniature line graphs (n=4) and to understand, compare, and interpret the 2 different ways of quantifying symptoms (n=3).

Interpretation

All participants (n=8) used visual elements to interpret data-finding patterns and viewing trends-to support understanding and decision making. They identified missed medications including the pattern of missed medications on Thursday using the calendar views (n=6). When seeing the pattern on Thursdays, participants were prompted to think about what could cause the patterns:

I wonder what's going on Thursdays that she always forgets the medications.
[MD3]

Identifying this pattern supported RN4's decision making to investigate the cause of the pattern:

I'm not sure why [she is missing her medications consecutively on Thursdays] so you would have to find out why is she missing her drugs on Thursday.
[RN4]

MD4 similarly described how using the visualizations supported understanding of the patient, reasoning about what might be causing abnormal readings, and ruling out potential causes:

...she just is not taking her medications for some reason...I can use the visualization and the colors to figure out some of her difficulties...[about] why she may not be adherent with her health [behaviors]and medications.
[MD4]

RN4 echoed how the visualizations could facilitate clinician reasoning, stating that a clinician could postulate about what might be causing the issues on Thursdays by bringing in symptoms and other data:

...you could really get I think a good picture.
[RN4]

Trends in line graphs helped participants interpret temporal glucose, blood pressure, and weight data (n=5). Participants reflected on several weeks' worth of data, comparing normal and abnormal points over time as well as visual elements indicating missed medication, to formulate whether they believe the patient had well-controlled blood glucose (n=2). For example, MD4 stated:

[if the medication being missed is] related to her insulin...and her blood sugars aren't controlled, [then] the general impression probably is that her blood sugars aren't controlled and her diabetes isn't controlled.
[MD4]

I also see that the same kind of pattern I've seen on this very day and the week before.
[MD3]

Use of Visualizations

Participants stated that visualizations could help gain an understanding of the patient outside the clinic and prepare specific questions to facilitate discussions with the patient about their self-management outside the clinic. This included discussing circumstances on Thursdays that made health management challenging (n=5), asking questions to help investigate the cause of rapid weight gain (n=1); and understanding symptom experiences or management (n=2). MD2 stated that the visualizations provide insights that "might open up a door to other questions that you normally wouldn't ask if you didn't [see trends]."

In addition, participants noted that visualizations could be helpful for patients. Having the visualizations during clinic visits could help patients remember symptoms; MD2 stated that the visualizations provide "another way to understand if the patient had any symptoms but forget to mention [them] to us or we forgot to ask [about them] during the clinic visit." Visualizations could also support patients feeling empowered; MD4 stated that a visualization tool could empower patients to engage in health behaviors such as taking medications and "be more aware of their symptoms" regularly.

All participants (n=8) mentioned the use of visualization to personalize visits with patients. Visualizations helped them identify issues specific to the patient that needed to be addressed, making interactions more focused on the patient and their specific needs (n=3). Visualizations could also give a clearer and focused picture of the patient, their health status, and needs that can better guide conversations and interactions with patients (n=4):

I think it would cause us to get a good picture, get a fast picture, evaluate that with the patient so we don't walk into an assumption, but dive a little bit quicker if we needed to.
[RN4]

MD3 stated that the visualization could personalize visits by bringing "attention to the important things" and focus "conversations with the patient directly to what's the issues or the problems that now I see [using the visualizations]..." RN4 stated that a benefit of the visualizations through personalizing visits with patients could be earlier identification of issues "rather than waiting until things [snowball]."

Ranking of Overview Visualizations

Participants varied in their preferences for the 4-week overviews. On average, participants ranked as most helpful (mean 1.8 [SD 1.2]) and least helpful (mean 3.9 [SD 0.4]; see ).

Principal Findings and Comparison With Prior Work

In our user-centered design study, we found that MD and RN participants' understanding of physiological and symptom data for individuals with MCC engaged in cancer care was supported by visualizations we developed by applying a visualization framework and relevant literature. Both MD and RN participants found that various visual encodings such as color, and familiar presentation such as calendar formats and line graphs supported their interpretation of the presented data. This research used foundational knowledge in data visualization in a novel way to develop visualizations that both MD and RN participants found helpful and effective in integrating various health-related data. Our MD and RN participants also noted the potential usefulness of the visualization in supporting personalized care. This user-centered design study offers findings from potential clinician users of the output of patient-generated health data from the OnPoint app. These results will be used specifically to inform the integration of visualizations into OnPoint system in the next phase of the project.

Although we cannot assume that an interactive tool incorporating visualizations for use by individuals with MCC engaged in cancer care would necessitate the same design as a tool for clinicians, we do believe that this study offers a starting point for features to consider for users who are patients.

Our study findings are congruent with guidance and best practices described in the visualization literature. Both MD and RN participants noted that color helped them see patterns in the data or pick out data that require attention, congruent with work described by Ware []. They also were able to use the line graphs to identify meaningful patterns in the data; this aligns with recommendations based on work by Cleveland and McGill [-] and Mackinlay []. In particular, position rather than other data encodings (eg, area) supports more accurate interpretation of the data being represented by the encoding. Although we did not compare our line graphs with other graph types in this study, participants responded positively to our design choice that was guided by the data visualization literature.

Implications for Developing Health-Related Visualizations

Finally, engaging potential end users early in the design ideation process was feasible and insightful. To minimize time and burden on HCP participants, researchers can carefully develop study protocols so they can maximize opportunities for participants to provide insights such as using mock patient personas and scenarios to guide eliciting feedback about mock-ups. Using personas and scenarios is advocated within the human-computer interaction domain [,], and it has been used to support the development of health informatics tools [,].

Limitations

There were limitations to our study. Our sample was limited to MDs and RNs; these visualizations could be useful to other HCPs supporting individuals with MCC engaging in cancer care, such as care coordinators, dieticians, pharmacists, and social workers. In addition, inputs from patients themselves and their family members must be collected to understand their informational needs. This study was conducted at a single cancer center; therefore, it has limited generalizability to other cancer centers. Mock-ups were on paper rather than on a device that a clinician would use to view visualizations in practice (eg, computer tablet and desktop computer). The data visualization literature used to guide the development of our mock-ups has not been tested extensively and empirically within health-related apps for cancer care; our work can support the building of evidence regarding the application of the visualization literature within this health domain. Although the visualizations are intended to be delivered via the electronic health record, we did not explicitly address how this might be accomplished. This work will be pursued in a future phase.

Conclusions

This study suggests that visualizations guided by a framework and literature can support HCPs' understanding of data to support personalized cancer care for individuals with MCC. By integrating health informatics and visualization literature and applying user-centered design methods, we were able to develop and elicit feedback on visualizations for health-related data including person-reported data. Future research could apply these methods toward the development of visualizations to support the care of other populations and the development of functional systems integrated into clinical and personal health care.

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cancer care facilities; informatics; patient-centered care; patient-generated health data; precision medicine; visualization

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Data Visualizations to Support Health Practitioners' Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

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Background: There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

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https://bigdata.dreamhosters.com/sites/default/files/2021-04/Visualization%20Techniques%20for%20Nursing%20Research.pdf

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https://www.sermo.com/resources/ethical-issues-in-healthcare/

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The first and most important maxim of the healthcare industry is Primum non nocere, or "First, do no harm." It sounds simple enough, but the healthcare industry is complex, and anyone in this demanding field faces difficult choices every day. Understanding the various ethical challenges in healthcare can empower you to make smart decisions that are better for patients.

High ethical standards are a must for anyone in healthcare, whether it be doctors, nurses, or administrators. To help you navigate these standards, here's a summary of current legal and ethical issues in healthcare and how to handle ethical violations.

What are ethical issues in healthcare?

When it comes to healthcare, ethical issues are a field of applied ethics concerned with the moral decision-making medical staff must apply when making decisions. Moral and ethical views in medicine tend to vary based on the country and culture. Tom L. Beauchamp and James F. Childress have developed a standard approach to ethical topics in healthcare that cross borders and cultures.

This ethical framework embraces four key principles of ethical decision-making within the healthcare sector.

1. Autonomy - Obtain the wishes of the patient so that their autonomy can be protected.

2. Beneficence - To seek to act in the patient's best interest.

3. Justice - Navigate due process to determine where the limits on healthcare lie.

4. Non-Maleficence - Designate harm and determine how to avoid it.

Healthcare practitioners must collaborate with patients to understand and balance their needs and desires. For example, would it be ethical to provide a blood transfusion to a Jehovah's Witness even if it would save their life?

Modern healthcare is collaborative, and talking to patients is key to avoiding ethical violations in healthcare. Medical practitioners should never make assumptions about a patient's needs and desires.

Examples of ethical issues in healthcare

Whenever a patient undergoes treatment, there are always medical ethical issues to negotiate. Issues with waiting lists, access to medical resources, and decisions on the correct course of treatment all pose ethical dilemmas. Take note: ethical is not the same as lawful. Something can be unethical but perfectly legal.

For example: if the emergency room is constantly backlogged, it's not legally required for healthcare staff to speed up their work. On the other hand, it could be considered ethical to raise the matter with the hospital's management team to find ways to help more patients, faster.

Some other examples of ethical issues in healthcare are the prioritization of treatment. Who needs immediate attention, and who should wait? Within organ donation, is it ethical to give a matching liver to an older patient who suffered from alcoholism when a younger patient would also benefit from that liver?

Ethical issues in healthcare are complex, and sometimes even the right decision might not feel like the most ethical approach.

5 ethical issues in healthcare

The medical world is constantly evolving, and as medical technology changes, doctors are left with a host of new ethical issues. In a recent Sermo survey asking physicians the top medical issues they faced, the top result was balancing care quality and efficiency. Responses included:

21% Balancing care quality and efficiency

17% Allocating limited medications or tools of support

17% Addressing end-of-life issues

15% Addressing access to care

15% Doctor and patient confidentiality

13% Allocating limited donor organs

When asked what other top ethical issues physician face in medicine, responses included:

Insurance dictating medical care. Insurance companies delaying or denying surgeries. Neurosurgery, U.S.

You cannot report to the ones who may actually be getting paid to be the underlying cause of the problem.
Internal Medicine, U.S.

Overcoming an ethical issue in medicine isn't black and white, as is the case with lawful/unlawful actions. Let's examine some of the key medical ethical issues facing healthcare staff today.

1. Do-Not-Resuscitate (DNR) Orders

DNR orders have always been controversial, as shown by the 17% of survey respondents claiming end-of-life issues as one of the top ethical issues they face. This order instructs any healthcare practitioner not to perform cardiopulmonary resuscitation (CPR) if the patient's heart stops beating or they stop breathing. These orders may only be written in consultation with a physician and a patient. Yet one ethical issue in medicine is what happens when a patient clearly displays a DNR order?

In 2017, one Florida man had a DNR order tattooed across his chest. Although the seriousness of the tattoo was questioned, the man could not breathe and could not talk, so the ICU unit decided to honor the message and opted not to initiate CPR.

From an ethical standpoint, DNR orders must balance the degree of pain involved in treatment when compared to the potential benefits. As a result, addressing end-of-life issues can be challenging for physicians.

For more information about DNR orders, check out this resource.

2. Patient Confidentiality

One of the biggest legal and ethical issues in healthcare is patient confidentiality which is why 15% of survey respondents noted that doctor-patient confidentiality is their top ethical issue in practicing medicine. Healthcare practitioners have an obligation to secure and protect the privacy of all patient medical records. Under the Health Insurance Portability and Accountability Act (HIPAA), violations could lead to suspension and termination.

Yet, there are gray areas, which could inadvertently lead to some ethical violations in healthcare. For example, would it cause more harm than good to withhold aspects of a patient's medical condition? Likewise, the opposite could be true.

For more information on patient confidentiality, check out this deepdive into patient confidentiality in healthcare.

3. Mandatory COVID-19 Vaccinations

According to Sermo's survey, 73% of respondents believe that ethical issues have increased since the start of the pandemic.

Public health bodies worldwide have discussed the merits and drawbacks of implementing mandatory COVID-19 vaccination policies. This is an issue that impacts the entire healthcare industry. Many countries have made vaccinations mandatory for all staff within the healthcare sector, including in Europe and Asia.

This is one of the most hotly debated current ethical issues in healthcare because it's brought serious questions into the spotlight. Where do individual medical liberty stop and the needs of wider society start? Is it ethical to force someone into treatment if they don't wish to undergo that treatment?

For further reading, here are ethical considerations for a COVID-19 vaccine mandate.

4. Healthcare Accessibility

According to figures from the United States Census Bureau, 294.6 million people had health insurance in 2017, which is a record high. Despite this, one in five U.S. adults decided not to seek healthcare in 2017 because they had no insurance. The soaring cost of both healthcare and associated insurance policies leads to ethical issues, which is why 15% of our survey participants highlighted this as their top ethical issue.

Is it ethical to reject someone who needs treatment based on their insurance status? Is it ethical for hospitals to charge so much for treatment? This is not only an ethical issue in medicine, but it's a discussion often taken up in politics regarding how society wants to operate its healthcare system. It's a debate that has raged for decades in the U.S., with no end in sight. Politically, the U.S. will see this as the defining ethical issue of our time, with profitability vs. health coming into direct conflict.

5. Allocating Limited Donor Organs, Medications and Tools of Support

13% of Sermo's survey respondents felt that allocating limited donor organs is the top ethical issue in healthcare, while 17% felt the same way about medication and tools. And it's not surprising considering that in the United States, more than 95% of North Americans are in favor of being an organ donor yet only 58% are actually registered donors. With less than 4% of the population being on the waiting list for a donor, it can be seen that most organs in the U.S. come from deceased donors and only a small percentage come from living ones.

This is why research on the ethical issues of limited donor organs is very important in order to increase the number of living donors. For more information on ethical issues in organ donation, check out Ethical principles in the allocation of human organs.

Additionally, medications and tools of support can be difficult ethical issues to manage. Medication, for instance, is the best way to cure disease. Plus, acutely acting medications are used as adjunctive therapy to a primary treatment method (e.g., surgery). However, some medications are in limited supply meaning not all patients receive the medication they need.

The same is true of tools of support for physicians. Different regions often offer varying degrees of support and a wide range of tools. Unfortunately, many rural areas suffer the most from lack of modern tools. Support, though, speaks more widely than to just physical components of a practice.

A doctor's work is never done, and when on-call at the hospital, there are few moments of rest in-between shifts. In order to be a hero to patients, doctors need support from colleagues and loved ones for this tough work. As a result, it's imperative that doctors find a way to balance helping patients without neglecting family, friends, or hobbies. Notably, when asked where they found support for dealing with healthcare issues 31% of survey participants mentioned colleagues while 30% looked to professional superiors for help.

How to handle ethical violations in healthcare

Moral and ethical issues place a burden on nurses, doctors, and administrative staff. Although the priority should be prevention, confronting ethical missteps is a part of the profession. This is necessary because 47% of physicians have had to address ethical violations in their own practice. So, how can you best handle these violations?

Our recent survey regarding this question provided the following results:

36% Report the conduct to appropriate clinical authorities, such as the peer review body of the hospital, or the local or state medical society

22% Protect the privacy of the patient

17% Report the suspected violation to appropriate authorities

14% Confront the people who are involved in the ethical violation

9% Report the conduct directly to the state licensing board

Here is a step-by-step example of how to address ethical violations at your practice. Firstly, acknowledgment and investigation of potential ethical issues in healthcare is a must. Bring together different disciplines and create an environment where people are allowed to and feel comfortable speaking up, regardless of their seniority.

Next, an ethical violation should be categorized as a process issue or a regulatory issue. Remember, something can still be unethical even if it happens to be lawful. For example, when a patient's wishes are at odds with the wishes of their families and the patient cannot speak out, what is the clinic or hospital's policy on the right way forward?

Ethical violations may require an update to internal rules and work processes. This will require consultation with frontline staff members, the administrative body, and the legal team. The best way to handle ethical problems in medicine is to adopt the following:

Provide regular refreshers on ASHA's Code of Ethics.

Break silos and allow for different staff members to come together to discuss the issue.

Establish a clear chain of command for when ethical issues need to be escalated.

Create an environment where the patient's wishes are prioritized.

Employ ethics experts to work alongside medical teams.

There's no getting around it: ethical issues will happen. Key ethical talking points are rarely straightforward, and there are no right and wrong answers. What matters is that as part of a patient-centered care system, ethics are at the forefront.

Conclusion

Facing tough moral decisions places heavy stress and self-doubt upon healthcare professionals. Get guidance on confronting specific situations or simply ask for support navigating ethical issues in healthcare from doctors who have gone through similar situations. Enter the virtual doctor's lounge and explore opinions from over 1 million doctors across 150 countries.

Sermo is the world leader in bringing together expertise, experience, and cases from the frontline all in one place. Join the world's largest physician-first online community by signing up to Sermo now.

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Identifying and navigating ethical issues in healthcare

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The first and most important maxim of the healthcare industry is Primum non nocere, or "First, do no harm." It sounds simple enough, but the healthcare industry is complex, and anyone in this demanding field faces difficult choices every day. Understanding the various ethical challenges in healthcare can empower you to make smart decisions that are better for patients.

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1. Autonomy – Obtain the wishes of the patient so that their autonomy can be protected.

2. Beneficence – To seek to act in the patient’s best interest.

3. Justice – Navigate due process to determine where the limits on healthcare lie.

4. Non-Maleficence – Designate harm and determine how to avoid it.

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https://www.sciencedirect.com/science/article/abs/pii/S1555415516304469

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Abstract

The ethical principles of self-determination and autonomy govern the practice of . A patient's right to shared decision-making and assent prior to invasive procedures, therapeutic interventions, and research projects is protected by law. Foundational informed consent nursing roles of communication, education, and patient advocacy compel advance practice nurses to formulate methods that safeguard patients' rights. Legal implications of informed consent may vary, leaving nurse practitioners juxtaposed between judicial and ethical responsibilities. The goal of this study is to examine legal and ethical components of informed consent and to assist nurse practitioners in developing proactive practice strategies related to informed consent.

Section snippets

Origin and Ethical Roots of the Informed Consent Process

A patient's right to participate in health care decisions arises from the individual's moral right to choose one's own plan for life and action.1, 3 Far removed from today's litigious climate, the concept of informed consent may be traced as far back as Hippocrates, Plato, and Galen, and likely originally intended to protect the provider rather than the patient. In the Hippocratic Oath, physicians have historically sworn to "prescribe a regimen for the health of the sick" and "utterly reject

What Is Informed Consent?

Informed consent is a process. The patient's signature on a document does not, by itself, constitute informed consent. Rather, the process of informed consent includes providing the patient with information about his or her health status, diagnosis, treatment options, and prognosis. The patient should participate in the development and decision-making of the plan of care, including the right to consent to or refuse medical or surgical treatment. Finally, the patient should receive adequate

When Is Informed Consent Required?

Generally, informed consent is necessary before performing surgery as well as certain medical treatments, therapeutic interventions, and invasive procedures. The Joint Commission's Standard RC 02.01.01EP4 requires that the properly executed informed consent form is placed in the patient's medical record before surgery, except in emergencies. ¹² In its Conditions for Participation for Hospitals, Section 482.13(b)(2), the Centers for Medicare and Medicaid Services requires the "patient's or the

Who Performs Informed Consent?

It is generally accepted that legal responsibility for obtaining informed consent lies with the provider performing the procedure. ¹¹ The Joint Commission requires that the "licensed independent practitioner or other licensed practitioners with privileges about the care, treatment, and services that the patient will receive" be responsible for ensuring that the patient has been provided with sufficient information and understands such information to provide informed consent. ¹⁵ The Joint

Documentation Considerations

A preprinted form containing information regarding the risks and benefits of certain medical treatments and procedures may assist in facilitating proper documentation regarding the informed consent process, but the form alone is not sufficient. The Joint Commission standards for hospitals require that a properly executed informed consent document be placed in the medical record before surgery. A properly executed informed consent document is defined as one structured to "contain[ ]

Shared Decision-making

Patients have not only the right but the responsibility to participate in their care. Discussions regarding research activities, therapeutic modalities, and invasive procedures are a critical part of the plan of care and should occur as often as necessary between the patient and provider in order to achieve effective informed consent. Shared decision-making is a principle first discussed in 1982 and is founded on concepts of patient-centered care. ³ The informed consent model is an element of

Conclusion

NPs must understand the nuances of the informed consent process and engage all patients in shared decision-making. To effectuate the patient's right to direct what happens to his or her body NPs should assess all patients' understanding and consent regarding proposed interventions. Whether the medical care or treatment involves tasks as routine as medication administration or as complex as surgical intervention, the NP should determine whether the patient understands the need for and has agreed

Linda Farmer, MSN, ACNP-BC, is an acute care nurse practitioner at Meridian, MS. She can be reached at .

The new 'code of ethics for nurses with interpretive statements' practical clinical application, Part II

O'Shea T. Consent in history, theory, and practice. The Essex Autonomy Project, Green Paper Report. University of...

A review of surgical informed consent: past, present, and future. a quest to help patients make better decisions

Doctors, patients, and lawyers-two centuries of health law

Centers for Medicare and Medicaid Services. Conditions for participation for hospitals. 2016....

There are more references available in the full text version of this article.

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Informed Consent: Ethical and Legal Considerations for Advanced Practice Nurses

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The ethical principles of self-determination and autonomy govern the practice of . A patient's right to shared decision-making and assent prior to invasive procedures, therapeutic interventions, and research projects is protected by law. Foundational informed consent nursing roles of communication, education, and patient advocacy compel advance practice nurses to formulate methods that safeguard patients' rights.

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https://www.floridatechonline.com/blog/healthcare-management/current-ethical-issues-in-healthcare/

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https://magazine.nursing.jhu.edu/2018/02/ethical-dilemma-advanced-practice-nursing/

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Is it ethical to act on the request of a 41-year-old woman with advanced breast cancer, who has been developmentally delayed since birth, for no further cancer treatment?

This was the question posed in one of our classes at the Johns Hopkins School of Nursing. In the course, Philosophical, Theoretical, & Ethical Basis of Advanced Practice Nursing, students are asked to debate an ethical issue in front of our peers and instructors with the goal of developing practical knowledge and skills to address ethical issues commonly faced in nursing. Students are asked to prepare for both sides of the argument, and a position is chosen for them based on a coin toss at the time of the debate.

In this situation, the positive and negative arguments both had valid points related to the treatment of individuals with disabilities and decision making capacity based on the virtue ethics principles of autonomy and beneficence.

The positive position in this debate is that it is ethical for the health care team to act on this woman's request for no further breast cancer treatment.

Respecting autonomy means honoring a person's right to make his or her own decisions, assuming the person exhibits the traits of an autonomous person. One of the main issues in the intellectual and developmental disabilities community has been challenging professionals who make one-sided decisions for clients about how they should live their life.

A developmental delay does not necessarily exclude this woman from having the ability to understand the consequences of her decision, to use reasoning skills to make this decision, to deliberate with others and explain her choice, and to make a choice as illustrated by Applebaum in the New England Journal of Medicine.

A provider's role is to disclose accurate medical, treatment, and prognosis information and to engage in shared decision making with the individual with the purpose of empowering the individual making his or her own decision.

In contrast, a provider does not have domain over an individual's values-and by denying her wishes to stop treatment-the provider acts as if he or she does not recognize and value the individual as a person capable of making her own decisions.

There are circumstances under which a provider could have cause for concern about a decision by an individual with an intellectual or developmental disability. Decisions that would cause "concern" might include refusing medication that would prevent an adverse outcome, refusing prevention or treatment that is standard of care, refusal to follow recommended diet, refusal to follow safe sex practices, or refusal of continuous positive airway pressure. Sometimes decisions such as refusal of breast cancer treatment can be confused with cause for concern because the decision strikes an unsettling tone in health care provider's values to 'help' or cure.

True concerns about this woman's decision should be addressed in a thorough investigation into her decision-making capacity, as with any individual.

In conclusion, the provider should not decide what is in this woman's best interest, given that a moral patient-provider relationship has been established and she can exhibit the traits of an autonomous person with full capacity.

The negative argument is that it is not ethical for the health care team to act on her request for no further cancer treatment.

As an adult, this woman will never recover development at her age, which means we need to both adjust care processes for her while protecting from paternalism through virtue ethics.

In living memory, people with disabilities were often institutionalized en masse, experimented on without knowledge, and forced to have treatments against their will or the will of their loved ones. Recent observational studies by Tuffrey-Wijne et al. from England and Australia show that individuals with disabilities are commonly not included in discussions about their treatment options. With this knowledge, we must practice respect for autonomy-starting with learning more about her, the affect her developmental delay has on her decision making capacity and autonomy, and her understanding of the likely impact of stopping treatment.

While we can appreciate that she has communicated a choice to stop treatment, we need to understand circumstances under which she communicated a choice to start treatment and identify if any evaluation of her capacity was completed when deciding to start treatment or since that time.

We practice beneficence in this case by ensuring that she receives the greatest benefit from her treatment. Additionally, we can apply the World Health Organization bio-psycho-social model of disability that aims to address the health inequities and poorer outcomes for people with disabilities around the world as described by Arnott. Using this model, we can meet her biological needs through access to symptom management as well as her psychosocial needs of support and quality of life.

Beneficence is an important strategy to support the greatest benefit for the population, including providing effective treatment for all people with disabilities and the population overall.

Due diligence is required however, to prevent paternalistic actions if she does have capacity to make this choice because violating her autonomy would result in great harm against her and the provider's virtues.

At the conclusion of our debate, we agreed on several points including:

The next step is the thorough assessment of this individual's capacity to order a stop in cancer treatment and respect her autonomy confined to her ability to understand, reason, deliberate, and choose.

Importance of learning more about her personal wishes and reasons for wanting to stop treatment.

The need for facts related to any prior evaluation of this individual's decision-making capacity prior to starting this treatment plan, or if the providers were happy to treat so long as she agreed with the treatment plan as determined by medical experts.

It is not unusual to have differences in opinion about the cause for concern or alarm for someone making the consequential decision to stop treatment for potentially life-threatening conditions or for severe symptoms.

ABOUT THE AUTHORS: MEGAN DUEVEL ANDERSON
Megan is currently a student in the Master of Science in Nursing (MSN) Entry Into Nursing Program and plans to graduate in May 2018. She graduated from Dartmouth College with a Master of Science in Health Policy and Clinical Practice Research in 2008. She is an active member of the Johns Hopkins School of Nursing Student Senate and a Fellow in the Helene Fuld Leadership Program for the Advancement of Patient Safety and Quality.
CHARLOTTE WERNER
Charlotte is currently a student in the MSN (Entry Into Nursing) Program and plans to graduate in May 2018. She graduated from Cornell University in 2012 with a Master of Science in Nutritional Sciences. She is a member of the Research Honors Program at JHSON.

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An Ethical Dilemma in Advanced Practice Nursing - Johns Hopkins Nursing Magazine

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Is it ethical to act on the request of a 41-year-old woman with advanced breast cancer, who has been developmentally delayed since birth, for no further cancer treatment? This was the question posed in one of our classes at the Johns Hopkins School of Nursing. In the course, Philosophical, Theoretical, & Ethical Basis of Advanced...

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https://www.ache.org/about-ache/our-story/our-commitments/ethics/ache-code-of-ethics/creating-an-ethical-culture-within-the-healthcare-organization

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March 1992
August 1995 (revised)
November 2000 (revised)
November 2005 (revised)
November 2010 (revised)
November 2011 (revised)
November 2015 (revised)
November 2020 (revised)
Statement of the Issue

The number and significance of challenges facing healthcare organizations are unprecedented. Growing financial pressures, rising public and payor expectations, consolidations and mergers, patient safety and quality improvement issues, workforce protection and safety, recognition of social disparities of health and their negative effect on health of the population, the legitimate demand of the community for enhanced involvement in priority setting and healthcare reform have placed healthcare organizations under great stress. Further, the current national climate has focused attention on our social system and highlighted social inequities; these issues have become central to our national dialog. All of these have the potential to intensify ethics concerns and conflicts.

Healthcare organizations must be led and managed with integrity and consistent adherence to organizational values, professional and ethical standards. The executive, in partnership with the governing body and clinical staff, must act with other responsible parties, such as ethics committees, to serve as a role model, fostering and supporting a culture that not only provides high-quality, value-driven healthcare but promotes the ethical behavior and practices of individuals throughout the organization.

Recognizing the significance of ethics to the organization's mission and fulfillment of its responsibilities, healthcare executives must demonstrate the importance of ethics in their own actions and seek various ways to integrate ethical practices and reflection into the organization's culture. To create an ethical culture, healthcare executives should: support the development and implementation of ethical standards of behavior, including ethical clinical, leadership, management, research and quality-improvement practices; ensure effective and comprehensive ethics resources, including an ethics committee, exist and are available to develop, propagate and clarify such standards of behavior when there is ethical uncertainty; support and implement a systematic and organizationwide approach to ethics training (including the consequences of social disparities in healthcare) and corporate compliance; and exemplify diversity, inclusion and equity as a core organizational value in creating an ethical culture.

The ability of an organization to achieve its full potential as an ethically aligned organization will remain dependent upon the motivation, knowledge, skills and practices of each individual within the organization, including all front-line workers and support staff. Thus, the executive has an obligation to accomplish the organization's mission in a manner that respects the values of individuals and maximizes their contributions.

Policy Position

The American College of Healthcare Executives believes all healthcare executives have a professional obligation to create an ethical culture. To this end, healthcare executives should lead these efforts by doing the following:

Demonstrating and modeling the importance of and commitment to ethics through decisions, practices and behaviors.

Promulgating an organizational code of ethics that includes ethical standards of behavior and guidelines.

Reviewing the principles and ideals expressed in vision, mission and value statements, personnel policies, annual reports, orientation materials and other documents to ensure congruence.

Supporting policies and behaviors that reflect these ethics is essential to achieving the organization's mission.

Using regular communications to help foster an understanding of the organization's commitment to ethics.

Seek community and stakeholder input in decision-making.

Collaborate with other community resources to make progress on community needs.

Communicating expectations that behaviors and actions are based on the organization's code of ethics, values and ethical standards of practice. Such expectations also should be included in orientations and position descriptions where relevant.

Ensuring individuals throughout the organization are respected and expected to behave in an ethical manner.

Fostering an environment in which the free expression of ethical concerns is encouraged and supported without retribution.

Ensuring effective ethics resources-such as an ethics committee-are available for discussing, researching, and addressing clinical, organizational and ethical concerns.

Establishing a mechanism that safeguards individuals who wish to raise ethical concerns.

Seeking to ensure that individuals are free from all harassment, coercion and discrimination.

Providing an effective and timely process to facilitate dispute resolution.

Using each individual's knowledge, skills and abilities appropriately.

Ensuring a safe work environment exists.

These responsibilities can best be implemented in an environment in which each individual within the organization is encouraged and supported in adhering to the highest standards of ethics. This should be done with attention to the organization's values, code of ethics and appropriate professional codes, particularly those that stress the moral character and behavior of the executive and the organization itself.

To ensure the creation of an ethically grounded culture, leaders should regularly assess their organization's culture using such approaches as employee surveys (internal town hall forums), informal job shadowing and focus groups. Executives in collaboration with clinical and administrative leaders need to recognize any barriers to maintaining an ethical culture. It is the responsibility of healthcare executives, leaders and all staff to take immediate and definite action when addressing ethical barriers. The ethical foundation of the organization depends on whether or not the leadership, and all associated individuals, are fulfilling its mission and values.

Approved by the Board of Governors of the American College of Healthcare Executives Nov. 16, 2020.

Related Resources
American College of Healthcare Executives Ethics Toolkit

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Creating an Ethical Culture Within the Healthcare Organization

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March 1992 August 1995 (revised) November 2000 (revised) November 2005 (revised) November 2010 (revised) November 2011 (revised) November 2015 (revised) November 2020 (revised) The number and significance of challenges facing healthcare organizations are unprecedented.

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https://journals.lww.com/cns-journal/Fulltext/2015/01000/Electronic_and_Social_Media__The_Legal_and_Ethical.4.aspx?casa_token=e9iDdnRg2T8AAAAA:aOYUDJRpFvPmAnlpIJeGpLnAfImuej-oesgG0kOSBx2bzWao55zHRhOpI1lln6H6JwVefEjvDA892wI8MrsVGw

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DEPARTMENTS: Legal and Ethical

Scruth, Elizabeth Ann PhD, MPH, CCNS, CCRN, FCCM; Pugh, Dale M. PhD, RN; Adams, Carmen L. DNSc, RNC; Foss-Durant, Anne M. MSN, NP, MBA, NEA-BC

Editor(s): Scruth, Elizabeth Ann PhD, MPH, CCNS, CCRN, FCCM

Author Information

Author Affiliations: Clinical Practice Consultant (Dr Scruth) and Quality Strategy Leader (Dr Adams), Quality, Regulation, Accreditation and Licensing Division, Northern California Kaiser Permanente, Oakland; Regional Director of Adult Services and Caring Science Integration (Ms Foss-Durant), Patient Care Services, Northern California Division, Kaiser Permanente, Oakland; Independent Consultant (Dr Pugh), Perth, Western Australia.

The authors reports no conflicts of interest.

Correspondence: Elizabeth Ann Scruth, PhD, MPH, CCNS, CCRN, FCCM, Quality and Regulatory Services, Kaiser Permanente, 1950 Franklin St, Oakland, CA 94612 ().

doi: 10.1097/NUR.0000000000000089

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Electronic and Social Media: The Legal and Ethical Issues... : Clinical Nurse Specialist

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DEPARTMENTS: Legal and Ethical Scruth, Elizabeth Ann PhD, MPH, CCNS, CCRN, FCCM; Pugh, Dale M. PhD, RN; Adams, Carmen L. DNSc, RNC; Foss-Durant, Anne M.

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Do-Not-Resuscitate Orders. ...

Doctor and Patient Confidentiality. ...

Malpractice and Negligence. ...

Access to Care. ...

Physician-Assisted Suicide.

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Week 1 How does informatics help FNPs in practice?

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Week 1 How does informatics help FNPs in practice?

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NPs can rely on the digital tools presented by health informatics, such as telemedicine, internal messaging systems, EPrescribing and more to better monitor patients and provide evidence-based care that turns into accurate, efficient outcomes.

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Week 1  How does health informatics impact FNPs?

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Week 1  How does health informatics impact FNPs?

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Improved expertise, enhanced communication, personalized care, self and patient protection, better outcomes

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Week 2 TIGER Nursing informatics for the advanced practice nurse

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Week 2 TIGER Nursing informatics for the advanced practice nurse

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https://www.springerpub.com/media/springer-downloads/McBride_Student-Study-Guide.pdf

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https://www.aanp.org/practice/practice-management/technology/health-information-technology-health-it

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American Association of Nurse Practitioners

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Health IT Resources for You and Your Practice

How Do I ... ?

HealthIT.gov has curated a collection of answers to common health IT questions-an excellent resource for NPs. Questions include "How do I implement health IT in my organization?" and "How do I ensure my health IT system is safe?"

The TIGER Initiative

TIGER (Technology Informatics Guiding Education Reform) is a grassroots initiative focused on education reform, fostering interprofessional community development and global workforce development. The spirit of TIGER is to maximize the integration of technology and informatics into seamless practice, education and research resource development.

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Health Information Technology (Health IT)

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Health IT Resources for You and Your Practice How Do I ... ? HealthIT.gov has curated a collection of answers to common health IT questions-an excellent resource for NPs. Questions include "How do I implement health IT in my organization?" and "How do I ensure my health IT system is safe?"

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Week 1 American Nursing Informatics Association website.

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Week 1 American Nursing Informatics Association website.

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https://www.ania.org/

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Home | American Nursing Informatics Association

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Week 1 Best health informatics websites and blogs 

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Week 1 Best health informatics websites and blogs 

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https://onlinedegrees.sandiego.edu/health-informatics-websites-blogs/

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If you work in health care or IT and are interested in expanding your career into the health informatics field, it's important to stay up on the latest trends and advancements in the industry. Reading blogs and checking in on popular websites is one of the best ways to do that. Here is a list of our favorite health informatics websites and blogs.

Published by HIMSS Media, Healthcare IT News is one of the best sources of news written specifically about health informatics. To keep you up to date on the latest happenings in the industry, Healthcare IT News offers articles, whitepapers, videos, webinars and more covering a range of topics including analytics, cloud computing, EHR, ICD-10, government and policy, interoperability, patient engagement, population health, precision medicine, privacy and security, revenue cycle, telehealth and women in health IT. Sign up for daily, weekly or monthly news summaries delivered by email, or follow Healthcare IT News on Twitter @HealthITNews.

AmericanEHR Partners is a company that provides tools to identify, implement and effectively use Electronic Health Records (EHRs) and other health care technologies. The American EHR blog provides ratings, reviews and comparisons of EHRs, plus articles covering current industry trends and events, innovative technology and other topics related to health care trends, products and services. Some examples include The Promise of Tomorrow's EHR, Five Ways Healthcare Data Analytics Can Help You, and Interoperability: A Necessity of Modern Healthcare. Get blog updates on Twitter by following @AmericanEHR.

As one of the most respected not-for-profit professional organizations focused on better health through information technology, HIMSS is a great resource for anyone interested or working in the field of health care informatics. Their Health IT Pulse is a roundup of news related to health, technology and related topics. Filtered and keyword search allows you to easily find articles related to the issues you care about most.
[RELATED] USD Master's Degree in Health Informatics Earns HIMSS Approval >>
"KevinMD.com shares the stories and insight of the many who intersect with our health care system, but are rarely heard from. Over 2,000 authors contribute to KevinMD.com: front-line primary care doctors, surgeons, specialist physicians, nurses, medical students, policy experts. And of course, patients, who need the medical profession to hear their voices," according to the website. The blog covers a wide range of health care topics from policy to social media to health care technology and informatics and is written from a physician's point of view.

Published by Scott Becker, JD, CPA, who provides counsel to a variety of businesses within the health care field, Beckers Health IT & CIO Review is a comprehensive site that covers a number of topics including:

Physicians

Leadership

Executive Moves

Transaction & Valuation

Human Capital and Risk

Patient Flow

Facilities Management

Telehealth

Supply Chain

In addition to the online publication, Becker's offers a complimentary monthly print subscription as well. Readers can find content broken down by medium, from webinars and whitepapers to multimedia, ebooks, lists, conferences and more.

The Digital Health Corner blog is written by David Lee Scher, MD, FACP, FACC, FHRS. A thought leader in the health informatics field and founder of DLS Healthcare Consulting, LLC, Scher consults and advises digital health technology companies on product and business development best practices from a technological, clinical and patient advocacy perspective. In his blog, Scher shares his knowledge and insight on using technology to improve health. He is apparently fond of the number five and using listicles to deliver his content. Blog titles include, Five Essentials of a Population Management Digital Health Technology, Five Ways Digital Communication Tools Can Improve Health Care and Five Mobile Health Technologies Providers Need to Implement NOW.

Published by the ONC, The Office of the National Coordinator for Health Information Technology, a division within the U.S. Department of Health and Human Services, Health IT Buzz was created to spark and accelerate the conversation around health informatics innovation. The publication covers EHR/EMRs and health innovation. They also have a special section called From the ONC Desk, which discusses the latest policy and event announcements on the transition to EHRs.
[RELATED] 8 Technologies That Are Changing Health Care >>
MobiHealthNews publishes new content daily on the latest in digital health news. Content on their website can be filtered by audience - payer, provider, pharma, consumer, investor - so you can quickly get the news that is most relevant to you.

FierceHealthcare is a news publication that covers health care reform, ACA implementation, health care fraud, Medicare/Medicaid, HIPPA compliance and more. The site also includes a job board for employers and job seekers. There is a strong editorial focus on documentation, data quality, coding and reimbursement.

Policy

Clinical Information Systems

Financial Information Systems

IT Governance Issues

Business Management

Managed Care and Health Care Payers

HIT Market

Professional Development

Health Information Exchange

Patient Safety, Care Quality, Performance Improvement

Imaging Informatics

Did we leave out one of your favorites? Let us know what we missed on Facebook or on Twitter @USD_Informatics. USD also has a blog where you can learn more about the health informatics field as well as our Master's in Health Care Informatics program that is offered both online and at our beautiful San Diego campus.

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The Best Health Informatics Websites and Blogs to Read and Follow

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If you work in health care or IT and are interested in expanding your career into the health informatics field, it's important to stay up on the latest trends and advancements in the industry. Reading blogs and checking in on popular websites is one of the best ways to do that.

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Week 2 TIGER- what nurse educators need to know about TIGER

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Week 2 TIGER- what nurse educators need to know about TIGER

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https://pubmed.ncbi.nlm.nih.gov/20173588/

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What nurse educators need to know about the TIGER initiative - PubMed

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The Technology Informatics Guiding Education Reform (TIGER) Initiative is designed to address a set of skills that is needed by all nurses who will practice in the profession in the 21st century. The skill set includes informatics competencies that range from basic computer skills to advanced-level…

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Week 2 TIGER competencies for every practicing nurse. 

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Week 2 TIGER competencies for every practicing nurse. 

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http://www.tigersummit.com/uploads/3.Tiger.Report_Competencies_final.pdf

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Week 3 Interoperability in healthcare guide

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Week 3 Interoperability in healthcare guide

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https://www.himss.org/resources/interoperability-healthcare

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What is Interoperability?

It is the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including the individual.

Four Levels of Interoperability

Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another

Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation

Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user

Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows

What is Health Information Exchange and Data Sharing?

Health information exchange, or HIE, provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective and equitable patient-centered care. HIE can also be used by public health authorities to assist in the analysis of the health of populations.

The term HIE is generally used as either a verb or a noun.

Verb: The electronic sharing of health-related data between two or more organizations facilitated by applied standards for use by a variety of stakeholders to inform health and care.

Noun: Organizations within the United States that provide health information exchange technology and services at a state, regional or national level and often work directly with communities to promote secure sharing of health data.

In the European Union, the act of information exchange is often referred to as data sharing. The European Commission's Support Centre for Data Sharing uses the term to refer to the collection of practices, technologies, cultural elements and legal frameworks that are relevant to transactions in any kind of information digitally, between different kinds of organizations. This term is more broadly applied to all types of data sharing, but can also be specifically used for health-related data.

Interoperability Standards

Standards provide a common language and a common set of expectations that enable interoperability between systems and/or devices. In order to seamlessly digest information about an individual and improve the overall coordination and delivery of healthcare, standards permit clinicians, labs, hospitals, pharmacies and patients to share data regardless of application or market supplier.

Standards Development

A standards organization, standards body, standards development organization (SDO) or standards setting organization is focused on developing, coordinating, promulgating, revising, amending, reissuing, interpreting, or otherwise producing standards that are intended to address the needs of some relatively wide base of affected adopters.

In order to be recognized as an SDO, an organization may be accredited by the American National Standards Institute (ANSI) or the International Organization for Standardization (ISO). Standards are also developed by other groups such as trade unions or associations. The development and adoption of open, consensus-based standards can be a complex process involving many different stakeholders and subject matter experts.

There are over 40 different SDOs in the health IT arena. Some entities create standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise (IHE), do not develop new standards, but rather bundle complementary base standards into IHE profiles that are used to define a specific function or use case, and then are balloted. This creates a scenario that helps drive adoption of the base standards by providing implementation guidance that describes how multiple standards can be used together to support interoperable health information exchange.

The different SDOs and profiling organizations have varying compositions and processes, but generally follow shared principles based on developing standards through a multi-stakeholder, consensus-based process to respond to specific industry or market needs.

Types of Standards

In order to understand the types of health data standards available for use, informatics professionals organize these standards into the following specific categories: vocabulary/terminology, content, transport, privacy and security, and identifiers.

Vocabulary/Terminology Standards

Vocabulary/terminology standards address the ability to represent concepts in an unambiguous manner between a sender and receiver of information, a fundamental requirement for effective communication. Health information systems that communicate with each other rely on structured vocabularies, terminologies, code sets and classification systems to represent health concepts. Some common vocabulary standards currently used in the marketplace include:

Current Procedural Terminology (CPT®): A code set, maintained by the American Medical Association (AMA), used to bill outpatient and office procedures.

Healthcare Common Procedure Coding System: A set of healthcare procedure codes based on CPT that is used for Medicare reimbursement.

ICD-10 and ICD-11: The International Statistical Classification of Diseases and Related Health Problems (ICD) is a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. The 11th revision will replace the ICD-10 in January 2022.

Logical Observation Identifiers Names and Codes (LOINC®): A universal code system for identifying health measurements, observations and documents. These codes represent the "question" for a test or measurement. LOINC codes can be grouped into laboratory and clinical tests, measurements and observations.

National Drug Code (NDC): Maintained by the U.S. Food and Drug Administration, NDC provides a list of all drugs manufactured, prepared, propagated, compounded or processed for commercial distribution.

RadLex: A unified language of radiology terms for standardized indexing and retrieval of radiology information resources, managed by the Radiological Society of North America. It unifies and supplements other lexicons and standards, such as SNOMED-Clinical Terms and DICOM.

RxNorm: A terminology used to normalize names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.

Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT): A comprehensive clinical health terminology product. It enables the consistent, processable representation of clinical content in electronic health records (EHRs). These codes often represent the "answer" for a test or measurement to the LOINC "question" code.

The Centers for Disease Control and Prevention (CDC) provide a number of code sets for vaccines (Vaccines Administered (CVX)) and manufacturers (Manufacturers of Vaccines (MVX)). These codes can be used in immunization messages.

The Unified Code for Units of Measure: A code system intended to include all units of measures used in international science, engineering and business to facilitate unambiguous electronic communication of quantities together with their units.

Content Standards

Content standards relate to the data content within exchanges of information. They define the structure and organization of the electronic message or document's content. This standard category also includes the definition of common sets of data for specific message types.

Consolidated CDA (C-CDA): A library of CDA templates, incorporating and harmonizing previous efforts from HL7, IHE, and Health Information Technology Standards Panel (HITSP). It represents harmonization of the HL7 Health Story guides, HITSP C32, related components of IHE Patient Care Coordination and Continuity of Care Documents, or CCD.

HL7's Version 2.x (V2): A widely implemented messaging standard that allows the exchange of clinical data between systems. It is designed to support a central patient care system as well as a more distributed environment where data resides in departmental systems.

HL7 Version 3 Clinical Document Architecture (CDA®): An XML-based document markup standard that specifies the structure and semantics of "clinical documents" for the purpose of exchange between healthcare providers and patients. It defines a clinical document as having the following six characteristics: persistence, stewardship, potential for authentication, context, wholeness and human readability.

Transport Standards

Transport standards address the format of messages exchanged between computer systems, document architecture, clinical templates, user interface and patient data linkage. Standards center on "push" and "pull" methods for exchanging health information.

Digital Imaging and Communications in Medicine (DICOM): The standard for the communication and management of medical imaging information and related data. DICOM enables the transfer of medical images across systems and facilitates the development and expansion of picture archiving and communication systems.

Direct StandardTM: Defines a set of standards and protocols to allow participants to send authenticated, encrypted health information directly to known, trusted recipients over the internet. Two primary specifications are the Applicability Statement for Secure Health Transport v1.2 and the XDR and XDM for Direct Messaging.

Fast Healthcare Interoperability Resources (FHIR®): An HL7 standard for exchanging healthcare information electronically. The basic building blocks of FHIR are "resources," which describe exchangeable health data formats and elements. FHIR also provides standardization for application programming interfaces (APIs). FHIR provides a number of benefits and improvements as a modern healthcare standard including facilitating interoperable exchange with legacy standards, lower overhead, shorter learning curve, an ability to transmit only the necessary pieces of information, potential for patient mediated data, and an energized community of supporters and implementers.

IHE provides a number of specifications that can be used in the exchange of health information.

PCHAlliance co-sponsors the Personal Health Device (PHD) efforts within the IHE Devices Domain, which are focused on developing IHE profiles that leverage and build upon the Continua Design Guidelines and are also updated to support FHIR. These profiles provide guidance to implement globally recognized, consensus-based approaches to connect and test both personal and clinical devices and integrate them into health information systems. The specifications being developed support both medical devices and mainstream consumer facing apps to enable scalable interoperability of the rapidly expanding connected health ecosystem.

Privacy and Security Standards

Privacy standards aim to protect an individual's (or organization's) right to determine whether, what, when, by whom and for what purpose their personal health information is collected, accessed, used or disclosed. Security standards define a set of administrative, physical and technical actions to protect the confidentiality, availability and integrity of health information.

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) outlines standards that safeguard the privacy and security of protected health information.

HIPAA Privacy Rule: Establishes national standards to protect individuals' medical records and other personal health information. It applies to health plans, healthcare clearinghouses, and healthcare providers that conduct certain healthcare transactions electrically. The rule applies safeguards to protect the privacy of personal health information, and sets limits and conditions on the uses and disclosures of such information without patient authorization. The rule also gives patients rights over their own health information, including the right to examine and obtain a copy of their records, and to request corrections.

HIPAA Security Rule: Sets national standards for protecting the confidentiality, integrity, and availability of electronically protected health information. Compliance with the Security Rule was required as of April 20, 2005 (April 20, 2006 for small health plans). The rule addresses the technical and non-technical safeguards that "covered entities" must have in place to secure an individual's electronic protected health information. Prior to HIPAA, there were no generally accepted requirements or security standards for protecting health information.

In Europe, the General Data Protection Regulation (GDPR) outlines privacy and security regulations for all processing and storage of data relating to data subjects-or people-in the European Union (EU). This regulation extends to health information and any organization that may process or store data on these subjects, meaning it has extensive reach to many organizations worldwide and related to the sharing of data across organizations.

Identifier Standards

Entities use identifier standards to uniquely identify patients or providers.

Enterprise Master Patient Index (EMPI): A data registry used across a healthcare organization to maintain consistent and accurate data on the patients treated and managed within its departments.

Medical Record Number (MRN): An organization specific code used as a systematic documentation of a patient's history and care during a hospital stay.

National Council of State Boards of Nursing ID (NCSBN ID): A unique identifier automatically generated for each registered nurse and licensed practical/vocational nurse, freely available via the Nursys database and maintained by NCSBN.

National Provider ID (NPI): A unique 10-digit number for a healthcare provider to create a standard identification. These NPIs are included in the free NPI Registry.

Object ID (OID): A globally unique ISO identifier and a preferred scheme for unique identifiers in HL7.

Standards, Implementation Guides and Profiles

Standards on their own are helpful because they describe and constrain what data moves.

Implementation guides describe how to implement these standards for a specific use case. An implementation guide is a companion to a standard that describes how to use a standard to satisfy a specific healthcare use case. It should specify which way a standard is to be applied in a particular use case-how to structure the data consistently and what vocabulary to use. While you can implement a standard in a number of different ways, when using an implementation guide to integrate a standard into a health IT system, the guide directs one way to constrain the standard for a particular situation, removing ambiguity and achieving consistency.

A benefit to the use of implementation guides like IHE Profiles is that they provide a common language for purchasers and market suppliers to discuss the integration needs of healthcare sites and the integration capabilities of health IT products. They provide precise definitions of how to implement standards to meet specific clinical needs. They offer developers a clear implementation path for standards that has been carefully documented, reviewed and tested as well as supported by industry partners. They give purchasers a tool that reduces the complexity, cost and anxiety of implementing interoperable systems. IHE profiles, in particular, organize and leverage the integration capabilities that can be achieved by coordinated implementation of communication standards, such as DICOM, HL7 W3C and security standards.

Testing and Conformance Efforts

The adoption and implementation of standards involves a testing ecosystem that spans standards development, implementation and feedback from real-world testing into the development process to support continuous improvement. There are various different testing and compliance efforts that support advancing health IT interoperability, including:

Conformity Assessment: IHE International administers the IHE Conformity Assessment Scheme, which forms the basis for IHE Conformity Assessment Programs and any official certification of conformance to IHE Profiles associated with such testing programs. IHE International authorizes designated test laboratories accredited under this standard to assess the conformity of products with selected IHE profiles.

eHealth Exchange Testing Program: The program was developed to test compliance for HIE standards as required by the eHealth Exchange Coordinating Committee for onboarding to the eHealth Exchange network. Its purpose is to enable organizations that wish to participate in the exchange to validate the compliance of their health IT with the eHealth Exchange Performance and service specifications.

HL7 FHIR® Connectathons: These events provide hands-on FHIR development and testing opportunities held in conjunction with working group meetings. Participants engage in development and testing, working directly with other FHIR developers and senior members of the FHIR standards development team.

IHE Connectathons: A cross-vendor, supervised and structured testing event where the industry tests implementation of IHE Profiles and other standards to successful standards implementation and use. All tests are evaluated on interoperability and conformance to IHE Profiles found in IHE's technical frameworks. The test floor is overseen by IHE's technical project managers, providing a safe, neutral test environment and an opportunity for industry collaboration and problem resolution.

Office of the National Coordinator for Health IT Health IT Certification Program: A voluntary certification program established by the Office of the National Coordinator for Health IT (ONC) to provide for the certification of health IT. Requirements for certification are established by standards, implementation specifications and certification criteria adopted by the ONC secretary. The program supports the availability of certified health IT for its encouraged and required use under other federal, state and private programs. The program is run as a third-party product conformity assessment scheme for health IT based on the principles of the ISO and International Electrotechnical Commission framework.

Organizations like the Global Consortium for eHealth Interoperability, which was co-founded by HIMSS, IHE International and HL7 International, work to amplify and align the work of organizations like IHE and HL7 to increase adoption of emerging and mature health IT standards. The primary goal of the consortium is to coordinate work with governments and national ministries of health to further align existing and emerging standards and implementation guidance with strategic healthcare policy goals to achieve improved health outcomes for people everywhere.

The Interoperability Ecosystem

The health interoperability ecosystem comprises individuals, systems and processes that want to share, exchange and access all forms of health information, including discrete, narrative and multimedia. Individuals, patients, providers, hospitals/health systems, researchers, payers, suppliers and systems are potential stakeholders within this ecosystem. Each is involved in the creation, exchange and use of health information and/or data.

An efficient interoperability ecosystem provides an information infrastructure that uses technical standards, policies and protocols to enable seamless and secure capture, discovery, exchange and utilization of health information.

Exchange Partners and Data Sources

Historically, the focus was on the exchange of clinical information between providers and hospitals. As the technology has progressed, these exchange partners have broadened to include individuals, such as patients and caregivers, and organizations, such as long-term care, public health departments, community-based organizations and payers. With this expansion, along with the shift to value-based care, has also come a broader range of data types that are available to inform health and care, including device data, patient-generated data and data pertaining to the social determinants of health. Social determinants of health can include data points such as an individual's housing status, access to reliable transportation and level of food security.

Network Architectures

There are three primary types of exchange network architecture used to coordinate the exchange of health information across entities.

Centralized: Patient data are collected and stored in a centralized repository, data warehouse or other databases. The exchange organization has full control over the data, including the ability to authenticate, authorize and record transactions among participants.

Federated (Decentralized): Interconnected but independent databases allow for data sharing and exchange, and grant users access to the information only when needed.

Hybrid: Incorporates variations of federated and centralized architectures to harness the advantages of both. These are becoming common as various combinations of available services are implemented.

An important technical development that has expanded exchange partners and data sources is the emergence of healthcare APIs. APIs outline a set of clearly defined specifications to allow for one software application to build on the data and functionality of another application, without needing to understand its system design. APIs are already ubiquitous in today's web economy, and will play an essential role with respect to fueling healthcare interoperability for person centered care.

Types of HIE Organizations

An HIE organization oversees and governs the exchange of health-related information among organizations according to nationally recognized standards. The purpose of a health information exchange organization is to perform oversight and governance functions for information exchange. There are several different types of HIEs currently operating across the U.S. and its territories:

1. Hybrid HIEs are often collaborations between organizations, such as an ACO and a vendor network, within a state or region. The Kentucky Health Information Exchange is an example of a hybrid model.

2. Private/Proprietary HIEs concentrate on a single community or network, often based within a single organization, and include overall management, finance and governance. Examples may include hospital/integrated delivery system networks, payer-based HIEs and disease-specific HIEs. Some software vendors have also established an HIE network for their clients across the U.S. Additionally, the industry may see other evolving entities such as Accountable Care Organizations (ACOs) supporting information exchange.

3. Regional/Community HIEs are inter-organizational and depend on a variety of funding sources. Most are not-for-profit. Indiana Health Information Exchange and Chesapeake Regional Information System for Our Patients are examples of a regional HIEs.

4. State-wide HIEs are run by the governments of their respective states or maybe the state's designated entity. Some state-wide (and regional) HIEs use an umbrella approach and serve as the aggregator for disparate private health information exchanges. Statewide Health Information Network for New York and Arizona's Health Current are examples of a state-wide HIEs.

U.S. Exchange Initiatives: An Environmental Scan

Many organizations are actively working to achieve ubiquitous exchange. This environmental scan provides a curated review of the current landscape of the U.S.-based networks and frameworks which enable interoperable, nationwide health information exchange via a variety of methods and collaborations.

International Networks Enabling Exchange

Beyond the examples outlined above, various exchange networks and infrastructure have been established globally.

The eHealth Digital Service Infrastructure (EHDSI) facilitates the two primary building blocks of cross-border digital health services in Europe: ePrescription and Patient Summary. ePrescription (and eDispensation) allows EU citizens to obtain their medication in a pharmacy located in another EU country. Patient Summary provides information on important health related aspects such as allergies, current medication, previous illness, surgeries, etc. The digital Patient Summary is meant to provide doctors with essential information in their own language concerning the patient, when the patient comes from another EU country and there may be a linguistic barrier.

An important precursor of major exchange initiatives in Europe have been the European Reference Networks. These are virtual networks involving healthcare providers across Europe. They aim to facilitate discussion on complex diseases and conditions that require highly specialized treatment, providing concentrated knowledge and resources. Coordinators convene virtual advisory panels of medical specialists across different disciplines, using a dedicated IT platform and telemedicine tools.

Argentina's National Interoperability Network uses standards to enable communication between participating health information systems, providing a bridge for identity management across systems, sharing documents and patient summaries, managing ePrescriptions and supporting national registries.

Australia's My Health Record is an opt-out, CDA-based document repository used to store shared health summaries, eReferrals, specialist letters, discharge summaries, event summaries, prescription records, diagnostic imaging and pathology reports. Once patients have their record, they are able to manage access and permissions.

Canada's Health Infoway manages an ePrescribing service, PrescribeIT, electronically shares prescription information from clinicians with pharmacies for dispensing. Another Infoway initiative, ACCESS Health, aims to accelerate citizen access to personal health information and digital health services. The exchange initiative is expected to use a cloud-based infrastructure, a FHIR-based API service and a blockchain-enabled consent service.

Hong Kong's Electronic Health Record Sharing System is the country's equivalent to a regional HIE, managing patient data on care episodes, lab results, radiology studies and drug items. The Hong Kong Hospital Authority launched eHRSS to public and private health sectors using an opt-in patient consent model.

Uses of Information Exchange
Watch the HIMSS TV deep-dive on interoperability.
Ultimately, the goal is to be able to share and access information that informs an individual's full, longitudinal health story. By having and understanding the complete and accurate picture of an individual's health-including their preferences and other determinants of health-includes a number of benefits. Clinicians can better inform care and decision making, patients can become active participants in their care plans, and health IT developers and implementers can leverage evidence to create and adopt systems that support clinical processes and improve care delivery.

A number of benefits can be realized for exchange stakeholders, including:

1. Care coordination: Care coordination often involves a variety of stakeholders, including patients, caregivers and care teams across settings in the management of a patient's health. This is a dynamic process that requires data movement across platforms and among service providers in real time to successfully manage care.

2. Improving business and administrative processes: Having access to health information can eliminate time-consuming tasks within a health system, including processing of intake information, coordination across care teams and reporting needs associated with various regulatory requirements.

3. Increased patient safety and satisfaction: Having a full picture of a patient's health through access to disparate data can better inform clinicians on the patient's medical history, preferences and past encounters. This can help avoid duplicate testing, reduce adverse events, inform care decisions and conduct appropriate follow-up to ensure adherence with care management.

4. Value-based care: Population-level data analysis is an essential component of health systems managing high-risk patient populations, public health systems and other stakeholders working to coordinate care needs for a community. Understanding population data can enable better risk analysis and more cost-effective care. At an individual level, having access to longitudinal health data on a patient can reduce duplicative testing and close gaps in information. A key consideration in the shift to value-based care is the incorporation of non-traditional health data, such as an individual's social determinants of health data (e.g. food security status, housing stability and access to reliable transportation) into patient records. This data can be leveraged by health systems to proactively identify a health risk within a target population and intervene to work toward positive outcomes.

With these benefits in mind, taking a use-case driven approach can frame how information sharing may inform care or business practices and can help frame the potential benefits and drive interoperability forward. The following examples provide a snapshot at how interoperable exchange may occur.

Care delivery data: A physician can query a network or external health system for the appropriate patient data from other sources, such as other clinicians and hospitals. The physician can then select certain patient documents or resources (such as medication lists and reconciliations, allergy lists or lab reports) that they want to access during a patient visit.

Consent management: Where consent is not governed by other laws or regulations, a centralized consent management process can enable consensus among the community's stakeholders for a patient consent model, and provide awareness of a patient's willingness to participate in data sharing activities.

Lab results: HIE services provide physicians with the ability to get results directly via their exchange networks.

Medication reconciliation: Having a longitudinal view of all of a patient's encounters can raise awareness of the potential medications a patient was previously or is currently using. This can allow for appropriate follow up with the patient to reconcile differences in records to produce an accurate picture of their medication lists for future encounters.

Patient intake: Using a query and response process, the patient's health record can be acquired ahead of an appointment for clinician review.

Patient preferences: Exchange networks can enable access to information about a patient's care preferences, such as advance directives, empowering patients to make decisions in their care.

Provider alerts: HIE organizations are able to provide alerts to a clinician on their patients when one presents to the emergency department or is admitted to or discharged from a hospital, to ensure appropriate follow-up can be conducted.

Query services: Physicians may want to obtain more information about a patient that is referred to their office. They can query an exchange network which can in turn query other networks or health systems to assemble a complete, longitudinal health record.

Record locator services/master patient indexes: Many exchange networks enable creation of one continuous community record or search capabilities for patient records, facilitating patient identification across multiple provider settings.

Referrals: Organizations have the ability to send referrals directly through HIE services, along with all appropriate documentation.

Secure messaging: Many use cases require the exchange of patient records or related health information between clinicians in different healthcare settings with different EHRs or IT systems. For instance, providers needing to send referrals can electronically share data, eliminating paper documents and expediting the patient's treatment.

Transitions of care: As patients make the transition from one care setting to another (such as from hospital to a primary care provider or skilled nursing facility), interoperable exchange of relevant documentation from one care setting can be easily accessed at the next.

Workflow Considerations and Guidance

As the HIMSS definition suggests, interoperability goes beyond its technical requirements. Often there are policy changes influencing the value proposition and therefore willingness to exchange. Organizational considerations must align with external organizations and work toward achieving trust across stakeholders to enable meaningful data sharing. The following outlines some of these considerations and the approaches that may be leveraged.

Obtaining Patient Consent

Ensuring and communicating patient consent for data sharing activities is critical to abide by regulatory requirements and maintain the trust of the patient. There are several consent models used in information exchange, dependent on regulatory requirements within a jurisdiction.

No consent: Patient health information at a participating healthcare organization is automatically included in and available for exchange.

Opt-in: No patient data sets are made available for electronic exchange until patients actively express whether they would like to make all, or a pre-defined set, of their information available.

Opt-in with restrictions: No patient data sets are made available for electronic exchange until patients actively give their consent to participate. Patients have the option to:

Allow information to flow only to specific providers/li>

Include only specific categories of data or data elements

Make all of their information available for exchange

Opt-out: All or some predefined data sets are qualified to be included and available for exchange, after patients are given the opportunity to opt out in full.

Opt-out with exceptions: All or some predefined data sets are qualified to be included and available for exchange after patients are given the opportunity to:

Allow the exchange of their information only for specific purposes

Limit exchange of their information only for specific purposes

Limit exchange of their information to specific providers or provider organizations

Opt out in full

Selectively exclude categories of data or specific data elements from the exchange

Regardless of the model leveraged, a patient has the right to revoke their consent at any time, and processes to ensure that preference is communicated and enforced is important for data sharing processes.

Ensuring Privacy and Security

Regulations such as GDPR in the EU, HIPAA in the U.S., and various other national and state laws and regulations set forth privacy and security requirements to safeguard health information when it is created, received, transmitted, or maintained by health systems and other organizations. Privacy and security considerations may include the following:

Executing and implementing data use and reciprocal support agreements (DURSA)-also known as trust agreements-with the health systems and other stakeholders. These agreements set forth who may access and exchange the data. Additionally, such agreements set forth standard privacy and security policies and procedures that govern data protection and use. Further, data is typically encrypted when data is exchanged from one organization to another.

While specific security requirements may differ depending upon the DURSA, a typical DURSA may call for each participant to maintain appropriate administrative, physical and technical safeguards to ensure the confidentiality, integrity and availability of the data, aligned with the HIPAA requirements and any other applicable laws and/or regulations.

Producing High-Quality Data

In order to ensure that the information received from disparate sources can be trusted that it is accurate and complete, there needs to be efforts by organizations to maintain high-quality data. This can be achieved by ensuring that:

1. Data are structured and coded with standardized terminology and styles where possible to be leveraged in a machine-readable format for analysis and interpretation

2. Documentation is robust and complete to communicate the full patient story in a way that provides meaning to the human end user of the information.

Public Policy and Government Efforts

Achieving interoperability to date has required government involvement, guidance and regulation. Public policy efforts have the potential to drive forward frameworks for trusted exchange, align and educate stakeholders on existing and emerging standards and broaden stakeholder participation to ensure an inclusive exchange ecosystem for care coordination and continuity. The following outlines examples of how governments have worked to facilitate exchange both within the U.S. and in countries around the world.

United States Policies and Initiatives

With the goal of achieving ubiquitous, interoperable, nationwide exchange, the U.S. Department of Health and Human Services (HHS) has enacted and implemented a variety of legislation, regulations and guidance to further the adoption of standards-based approaches to interoperability.

21st Century Cures: Key Provisions

Among the health IT provisions outlined in 21st Century Cures, many sections provide directives to the ONC, Centers for Medicare and Medicaid Services (CMS) and other agencies related to improving interoperability.

The draft Trusted Exchange Framework and Common Agreement includes the policies, procedures and technical standards that build from existing health information network capabilities and enables them to work together to provide that single on-ramp to electronic health information regardless of the developer, health information exchange, or where a patient's records are located.

The U.S. Core Data for Interoperability is a "standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange." A "Data Class" is an "aggregation of various Data Elements by a common theme or use case."

The ONC Interoperability and Information Blocking Final Regulation implements key provisions of the 21st Century Cures Act focused on advancing interoperable exchange; supporting the access, exchange and use of electronic health information; and addressing occurrences of information blocking. The rule calls for accelerated use of healthcare APIs. ONC Certified Health IT must have standardized APIs for patient and population services, and meet specific requirements called out in the API Conditions and Maintenance of Certification. Most importantly, the regulation requires that United States Core Data for Interoperability is available for healthcare providers and patients "without special effort."

The Cures Act tasks ONC with the creation of an EHR Reporting Program as a condition of their certification and maintenance of certification. ONC has a process to solicit feedback from the industry on what criteria and methods of collection should be explored for this program. The program's reporting criteria aims to address the following five categories: security, interoperability, usability and user-centered design, conformance to certification testing, and other categories as appropriate to measure the performance of certified EHR technology.

In parallel with the release of the ONC Interoperability and Information Blocking Final Regulation, CMS also outlined an Interoperability and Patient Access Final Regulation, as part of their MyHealthEData Initiative, focused on driving exchange of and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage, Medicaid, Children's Health Insurance Plan, and Qualified Health Plan issuers on the federally facilitated exchanges.

Interoperability Standards Advisory

The Interoperability Standards Advisory (ISA) process represents a single, public list of standards and implementation specifications published by ONC. ONC coordinates the identification, assessment and determination of these recommended standards and implementation specifications for industry use to fulfill specific clinical health IT interoperability needs. Stakeholders are encouraged to implement and use these recommended standards as applicable to their needs.

The ISA is organized into sections with related interoperability needs, each including one or more standards or implementation specifications to accomplish the listed need. Additional characteristics on these standards and implementation specifications are listed to inform stakeholders on maturity and adoption. These include:

1. Adoption Level: Approximate, average adoption level for that specific use case in healthcare within the United States.

2. Cost: Conveys whether a fee is involved to purchase, license, or obtain membership for access or use of the recommended standard or implementation specification.

3. Federally Required Status: Conveys whether a standard or implementation specification has been adopted in regulation, referenced as a federal program requirement, or referenced in a federal procurement (i.e., contract or grant).

4. Implementation Maturity: Conveys maturity based upon its implementation state.

5. Standards Process Maturity: Conveys maturity in terms of its stage within a particular organization's approval/voting process.

6. Test Tool Availability: Conveys whether a test tool is available to evaluate health IT's conformance to the standard or implementation specification.

Advancing Standards for Precision Medicine

This ONC project aims to standardize data found outside of traditional care delivery settings. As part of the precision medicine initiative, it focuses on standards in two areas:

1. Mobile health, sensor and wearable data

2. Social determinants of health data

Interoperability Proving Ground

ONC hosts an open, community platform where individuals and organizations can share and learn from projects occurring in the United States and internationally.

Data: Elemental to Health Campaign

HIMSS and PCHAlliance strongly urge prioritization of necessary funding and technical assistance to implement modern health information and technology strategies to ensure timely interoperability and sharing of public health surveillance data. An important component is the work in partnership with the Association of Public Health Laboratories, Council of State and Territorial Epidemiologists and the National Association for Public Health Statistics and Information Systems that support this multi-year effort to modernize public health data systems, surveillance and analytics at the CDC, and state, local and tribal health departments. Funding included in the Fiscal Year 2020 Appropriations Package and the Coronavirus Aid, Relief and Economic Security (CARES) Act, will help address some of the challenges, but in the long term, we need a predictable and sustained funding source to fully tackle this issue.

State-Level Interoperability Initiatives

In the U.S., state public health agencies manage and track information for immunizations, infectious diseases and vital statistics, however, they often lack adequate funding to ensure their information systems are updated and conform to national standards and profiles. Health information exchanges -which are public or private entities, depending on the state and region-aim to ensure there is robust and standardized data exchange between and among public health entities and state and local health departments.

Global Governmental Policies and Initiatives

At an international level, there are a variety of initiatives being led by health ministries and governments to further country-level and cross-border interoperability efforts.

The Global Digital Health Partnership (GDHP) is a collaboration of over 40 governments and territories, government agencies and the World Health Organization, formed to support the effective implementation of digital health services. Interoperability is one of the various work streams explored by GDHP. The proposed work stream activities focus on the evolving challenges of sharing patient data between care providers, organizations, caregivers and patients. These challenges are partly a technical problem for health systems, clinicians and patients, however they also pose significant risks for patient safety, and detract from high-quality coordinated care and the efficient delivery of services. A GDHP report outlines a variety of government efforts from 15 participating countries.

In Europe, the EU EHR Exchange Format developed the EU's eHealth Network to support the digital transformation of health and care in the EU by seeking to unlock the flow of health data across borders, and support implementation of the Cross-Border Health Care Directive. The recommendation seeks to facilitate the cross-border exchange of EHRs in the EU by supporting member states in their efforts to ensure that citizens can securely access and exchange their health data wherever they are in the EU. It will help citizens to quickly access and share their health data with healthcare professionals, for example, when consulting a specialist or receiving emergency treatment in another EU country.

The backbone infrastructure for the EHR exchange is the EHDSI. The EHDSI consists of services and infrastructure that use information and communication technologies to enable cross-border healthcare services. Initial focus is on enabling the exchange of baseline health information, including:

ePatient Summary to provide access to verified key health data of a patient during an unplanned care encounter while abroad

ePrescriptions to enables patients to receive equivalent medication treatment while abroad to what they would receive in their home country

A number of EU countries are already exchanging the ePatient summaries and ePrescriptions. As part of the EHDSI, Finland and Estonia were among the first to implement infrastructures to support the exchange of patient summaries, leveraging the X-Road platform, a free and open-source data exchange layer solution that enables organizations to exchange information securely over the internet. Future phases will include lab results, medical imaging and reports, and hospital discharge letters.

The European Commission also outlined priorities for 2019-2024 EU Digital Strategy, which included the creation of a European Health Data Space (EHDS) to foster the exchange and sharing of different kinds of health data (electronic health records, genomics, registries, etc.) in Europe. It aims to not only support exchange for the delivery of primary care, as well as, the development of new treatments, medicines, medical devices and services. Beyond the citizens, this would help meet the needs of different users and actors in the health system, while simultaneously protecting citizens' data. The commission is currently working with the member states and stakeholders to define the best governance structure and set up the appropriate infrastructure for the EHDS. Additionally, some existing regulatory gaps regarding member states implementation of the GDPR for the secondary use of data are being addressed.

The European Interoperability Framework (EIF) gives specific guidance on how to set up interoperable digital public services. It offers public administrations 47 concrete recommendations on how to improve governance of their interoperability activities, establish cross-organizational relationships, streamline processes supporting end-to-end digital services, and ensure that both existing and new legislation do not compromise efforts.

In an effort to ensure secure and federated access to genomic data, the European 1+ Million Genomes Initiative aims to create a technical infrastructure to improve disease prevention, allow for more personalized treatments and provide a sufficient scale for new clinically impactful research. In addition to the technical foundation for this effort, the signatory countries will also assess ethical and legal implications of genomics, and data storage, security and ethical uses.

Beyond these broader initiatives occurring across Europe, many countries are also leading specific regional and national efforts to expand exchange abilities./p>

The Nordic Interoperability Project is a consolidated grassroots effort to demonstrate the value for the patient from a seamless cross-border exchange of health data. The consortium includes organizations from all five Nordic countries-Denmark, Finland, Iceland, Norway, Sweden-and works to enable patients to live and act in an open, seamless, cross-border healthcare ecosystem, by showcasing and implementing solutions and innovations from the Nordics. The goal is to enable the Nordics by 2030 to be the most sustainable and integrated health region in the world, providing personalized healthcare for all its citizens.

In Portugal, the Shared Services of the Ministry of Health (SPMS) is a state-owned enterprise that manages the Portuguese Health Ministry Central Purchasing and serves as the national IT authority and the Portuguese representative for eHealth cross-border affairs. SPMS provides shared services in the areas of purchasing and logistics, financial services, human resources, and information and communication technologies to health and care entities to centralize and optimize the procurement of goods and services within the Portuguese National Health System.

In Asia and the South Pacific, many governments are also adopting digital health strategies to encourage interoperable exchange. For example, India's The Ministry of Health and Family Welfare (MoHFW) publishes EHR standards and created a Centre for Health Informatics under the eHealth Division of MoHFW as part of their Digital Health Strategy to initiate several digital health initiatives in the country, and started the National Health Portal as a citizen portal with the objective of improving the health literacy of the masses in India. New Zealand's health agencies, organizations and individuals signed a Commitment to New Zealand Health Interoperability to outline principles to drive the adoption of exchange infrastructures and processes in their health sector.

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Discover key insights about interoperability and better understand how data sharing can make a difference in healthcare.

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Week 1 Role of informatics in promoting quality health care and the need for education on informatics

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Week 1 Role of informatics in promoting quality health care and the need for education on informatics

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825491/

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Abstract

In today's dynamic health systems, technology plays an important role in education and nursing work. So it seems necessary to study the role of nurses and highlight the need for appropriate information technology educational programs to integrate with the ever-increasing pace of technology. A review accompanied by an extensive literature search in databases and a library search focused on the keywords were used. The criteria used for selecting studies primarily focused on nursing informatics and the importance of expertise in the effective use of information technology in all aspects of the nursing profession. In a critical assessment of emerging technologies, the key elements of nursing informatics implementation were considered as healthcare promotion, advanced systems, internet and network. In view of the nature and the development of the information age, it is required to receive necessary IT training for all categories of nurses. Due to the fast development of technology, in order to effectively take advantage of information technology in nursing outcome and quality of health care and to empower nurses; educational arrangement is recommended to set short-term and long-term specialized courses focusing on four target groups: studying, working, graduate, senior undergraduate, and graduate doctoral. The result of this study is expected to assist educational providers with program development.

Keywords: education, nursing, quality of health care, nursing informatics, technology

1. Introduction

1.1 History and Definition

Nurses has been working in the field of informatics near four decades, the term "nursing informatics" has been considered a specialization in nursing resources since 1984 ( Guenther & Peters, 2006). Many aspects such as data recovery, ethics, patient care, decision support systems, human-computer interaction, information systems, imaging informatics, computer science, information science, security, electronic patient records, intelligent systems, e-learning and telenursing have been added to the field. Hana has defined Nursing Informatics as the application of IT in the nursing duties including education, management & practice in 1985. Integration of information science, computer science and nursing science to support nursing practice and knowledge management was the definition offered in 1989 by Graves and Corcoran. The American Nurses Association (ANA) published its aim and standards in 1994-1995 and presented the Nursing Informatics as a specialty that integrates nursing science, computer and information science to provide data communication management, knowledge and nursing work in 2001. Now most of nursing professionals believe that it is defined as the integration of information technology and all aspects of nursing such as clinical nursing, management, research or education ( Guenther & Peters, 2006).

1.2 Competencies

The competency of nursing informatics specialists was determined through studying three categories including computer skills, informatics knowledge and informatics skills. It investigates four levels of nursing practice: beginning nurse, experienced nurse, informatics specialist, and informatics innovator.

The following competencies were rejected: diagnostic coding, desktop publishing, managing central facilities to enable data sharing and writing an original computer program ( Staggers et al., 2002). Some components of accepted competencies are shown below in brief.

1.2.1 Computer Skills

Selected computer skill competencies contain computerized searches and retrieving patient demographics data, the use of telecommunication devices, the documentation of patient care, the use of information technologies for improving nursing care, and the use of networks and computer technology safely.

1.2.2 Informatics Knowledge

Selected informatics knowledge competencies are the recognition of the use or importance of nursing data for improving practice, and the recognition of the fact that the computer can only facilitate nursing care and that there are human functions that cannot be performed by computers, the formulation of ethical decisions in computing, the recognition of the value of clinicians' involvement in the design, selection, implementation, and evaluation of systems in health care, the description of the present manual systems, the definition of the impact of computerized information management on the role of the nurse and the determination of the limitations and the reliability of computerized patient monitoring systems.

1.2.3 Informatics Skills

Informatics skills competencies includes the interpretation of information flow within the organization, the preparation of process information flow charts for all aspects of clinical systems, the development of standards and database structures to facilitate clinical care, education, administration or research. It also includes the development of innovative and analytic techniques for scientific inquiry in nursing informatics and new data organizing methods and research designs with the aim of examining the impacts of computer technology on nursing, and the conducting of basic science research to support the theoretical development of informatics. Information literacy skills, competencies, and knowledge are investigated among educators, administrators and clinicians of nursing groups nationally.

1.3 The Importance of Nursing Informatics

The history, definition and competencies of nursing informatics indicate the importance of this field. It shows nurses are integrated into the field of IT automatically. So they should be able to deal with it successfully to improve quality of care outcome. In this regard it is required to study the influence of nursing informatics on health care and make bold the appropriate information technology educational needs for nurses.

2. Method

An extensive literature search was performed by using databases Pubmed, Google Scholar, Ovid, Science Direct and SID. Search terms were "education, nursing"; "quality of health care"; "nursing informatics" and technology. The study was carried out from January to April, 2014. A library search was also performed. As many as 135 articles were retrieved. With a critical point of view, 40 articles in English were selected that specifically focused on nursing informatics education and its influence on nursing outcomes and the quality of health care ( Staggers et al., 2002).

3. Results

The study mentions the followings as the key elements of nursing informatics implementation:

3.1 Health Care Promotion

The advantages of applying information technology in all aspects of nursing, including clinical areas, management, education and research and its influence on health care have been reviewed. Today, the subjects of clinical nursing information systems, decision support systems and medical diagnostic systems are associated with collecting patient information. Regarding the technology-rich environment, health care and hospital information systems developers, the quality of care is improving. For increasing patient safety and its leading to an evidence-based nursing, nursing informatics has been enhanced for students and graduates by Columbia school of nursing. The study has proved that informatics competence is a prerequisite to improving patient care ( Bakken et al., 2003). Technology and using multimedia integrated into nursing curriculum can promote the use of informatics tools as an integral practice component and increase patient safety ( Norton et al., 2006). Managers can improve efficiency and performance through information systems and new technologies. In addition, several studies have confirmed the impact of careful shift planning and efficient management on nurse's work and the quality of health care. Information is the source of all management activities. Nursing care is an industry service and its product is patient care. Information technology can promote the nursing management outcome. Internet-based nurse scheduling systems are mostly designed according to the self-scheduling model and need refining by the manager who overviews proper distribution, it causes uniform resource allocation in scheduling and increases patient direct care time ( Pierce et al., 2003). Implementing information systems can provide better access to evidence; it can affect the patient care quality and support evidence-based nursing. Software tools to facilitate research are available in all medical fields (Kardan & Darvish, 2008). Nursing information system had an influence on clinical patterns and decreased the time nurses spent on indirect care ( Darvish & Salsali, 2010). This is critical to the health care professionals to assess, apply, report and manage data by the help of new tools of the information age ( Hall, 1995).

3.2 Advanced Systems

Although using decision support systems can lead to a safer care, it may impair critical thinking. The need for excessive working time could cause some delay in the nursing job and reduce the quality of health care ( Norton, 2006). Researchers recommended considering the following:

-Involving nurse managers in the system selection and designing process

-Designing a simple and efficient process

-Recording a system-compatible guideline

-Improving the system speed

-Selecting hardware which can encourage nurses to use them

-Upgrading the system through innovation in information technology

Decision support systems have been defined to assist physicians to solve problems that require specific decisions since 30 years ago. It is replacing the role of human knowledge by formulating the knowledge in the system (Ting et al., 2008). Expert systems are the most common types of clinical decision support systems and have applications in show notes, diagnostic support, critical treatment plans, decision support, prescriptions, recovery and the identification and the interpretation of pictures, however, as stand-alone tools, are not able to replace human expertise. These systems should be integrated with knowledge management. Several studies have shown that the integration of implicit and explicit knowledge and management of different types of knowledge will help to determine the best treatment plan. Logical design is required for the success of these systems and seems to hardly have been considered. The application of guides and easy access to up-to-date clinical evidence and the cutting of duplicate tests could reduce medical mistakes and improve quality of care, but there are some limitations ( Montani & Bellazzi, 2002). To increase the usefulness and acceptability of such systems, the ease of use, support and maintenance combined with the ability of systems hardware, software applications, integration with hospital information systems and patient records should be considered ( Holbrook et al., 2003). Including smart and intelligent tools in diagnosis and treatment methods can reduce medical errors and harm as well as financial loss for humans. Artificial intelligence and expert systems are used to help the diagnosis.

3.3 Internet and Network

The first internet-based Nursing Informatics courses were offered by Duck University in 1997. Represented advantages were clearly defined and measurable learning outcomes and real-world problems were introduced as the main component of instructional strategies. There were some disadvantages like hardware and software problems, deficiency in prerequisite skills, troubleshooting difficulties, and low internet access speed and poor time management to master the material ( Goodwin, 1997). The evaluation of an innovative consumer health informatics intervention proved that the patient and nurses are satisfied with the use of electronic and communication devices and home care ( Kossman et al., 2006). Nurses can have networks such as AJN and AMN now. In the world wide web, it is possible to have a common network for nursing organizations and develop a forum to discuss nursing issues and design online meetings ( Rizzolo & DuBois, 1995). The other aspects of nursing practice potential in the new decade are offering services from distance through telemedicine or telenursing. In this regard, easy remote diagnostic software and hardware are designed to facilitate E-health services. Tele-nurses can provide various services such as education, patient monitoring and counselling through Internet facilities. Telenurses are satisfied with their role. They care remotely using special skills and knowledge. It can cover nurse shortages and the global demand for nurses ( Darvish & Salsali, 2010). In a telenursing Survey most of nurses believed that it is better to design educational programs for nurses to be able to work as telenurse ( Grady, 2007). Using online library resources and outreach programs would be beneficial and produce positive outcomes for nurses ( Wozar & Worona, 2003). Information and communication technology progress provided the possibility of improving health through e-education irrespective of time and place. Patient education systems on the internet can increase patient satisfaction and influence their self-care behaviour. E-health educational programs make people aware of disease management and increase coordination with the health care professional team. It influences the life style and the prevention of diseases such as cancer, HIV and chronic diseases. On the other hand, it empowers medical groups by enhancing and upgrading their knowledge. The web-based computer simulation educational program in crisis decreased medical errors in emergency departments. Information technology application refers to providing simultaneous access to education in specified locations which require huge spending. It decreases cost loss ( Kardan & Darvish, 2007). Online access to journal articles prevents repeated research programs and makes assessment easy. Online databases provided up-to-date article access and informed nurses about new technologies, easy software and the results of investigations ( Darvish, 2010).

3.4 Related Organizations Activity

National advisory council on nurse education and practice addresses nursing practice challenges; and mentions electronic health records, patient monitoring systems, bar code medication administration, computerised provider order entry, data capture tools, care planning tools and telehealth; and the need to consider nursing shortage, reducing medical errors, improve tracking of patient data, improve efficiency of data collection, improve access to care, support national surrveilance capabillities. These are also mentioned as ways to address nursing education challenges: E-learning to deliver education, simulation to deliver education, inclusion of Health Care IT in curricula, and faculty development for health care. It centeralizes these as solutions which can improve capacity of nursing schools, reduce faculty shortage, and increase health IT skills of graduating students. There are many group projects and organizations which support nurses' involvment for optimal use of IT in their job. Some are introduced in the following:

-The National Coordinator for Health Information Technology (ONC)

-The American Health Information Community (AHIC)

-The Nationwide Health Information Network (NHIN)

-The Faculty Development: Integrated Technology into Nursing Education and Practice Initiative (ITNEP)

-National Advisory Council on Nurse Education and Practice (NACNEP)

-National League of Nursing (NLN)

-National Institute of Health (NIH)

-National Institute of Nursing Research (NINR)

-North American Nursing Diagnosis International (NANDA)

-Technology Informatics Guiding Education Reform Initiative (TIGER)

It is reported that without proper training for nurses, efforts to integrate healthcare IT with nursing practice will be hampered. It gives evidence that nurses are not getting adequate training for IT usage ( NACNEP annual report, 2009).

3.5 Need for Educational Programs

Due to the fast growth and development of technology, in order to effectively take advantage of information technology in nursing outcome and quality of health care, educational arrangement is recommended to strengthen nurses at different levels for implementing information technology tools in all aspects of their profession.

4. Conclusion

In today's world the potential for information and communication technology application is increasing so that it can enhance the quality of nursing domains outcome ( McNelis et al., 2012). Nurses have the most communication with patients, and interact with technology more frequently. Using technology should create a positive attitude in nursing productivity. It is essential for nurses to be involved in the initial design of systems to improve the quality of health care and change their culture in this regard ( Darvish & Salsali, 2010), ( Jenkins et al., 2007).

Mediating technically and technologically on the borderline between medicine and nursing, nurses have become known as the medical Goddesses in the form of Tele-nurses. Nurses have got more authority in decision-making with the use of new technologies ( Gassert, 1998). For successful implementation of the electronic health reporting system, nurses must be knowledgeable about information technology, computer skills and informatics knowledge and skills. In telenursing, the importance of data quality criteria, transparency and integrity, authenticity, confidentiality, the updating of information, accountability, productivity, standards and accessibility of health web sites should be considered (Darvish, 2008). The NACNEP recommended to prepare nurses to adopt intelligent and quality-based information technology use in patient care by implementing five strategies: providing core informatics courses to nursing schools, educating nurses specialized in informatics skills who are able to solve related issues, offering more powerful nursing care through the implication of telecommunication projects, preparing more nursing faculties in the informatics field to facilitate students skills enhancement and enhancing collaboration to advance informatics. The benefits of extending nursing informatics strategies directly and indirectly influence patient and people health positively ( Matarrese & Helwig, 2000). Courses affect nursing students' perceptions about informatics ( Jetté et al., 2010); and they may learn at the BSc level about patient-centered evidence-based care through the use of informatics tools, and get informed about benefits such as promotion of safety, quality and effective clinical decisions ( Norton et al., 2006; Ainsley & Brown, 2009). The may even learn how remote care and personal phone can improve nursing care in different areas such as psychiatric nursing ( Tseng et al., 2012; Goossen, 2007; Wittmann-Price, 2012). At the same time, nursing workers are busy in the wards giving care. If they are not alert to new technologies, it will be difficult to accept the new nurses' ideas who are educated recently with a positive attitude to the advantages of information technology. This group of nurses can be encouraged to be integrated into the potential of E-learning as well as continuing education ( Button, 2013), based on the summit of technology informatics guide education reform ( Schlak & Troseth, 2013). It seems necessary to prepare knowledgeable nurses to deal with selecting, developing, implementing and evaluating IT to interpret data as usable knowledge and information. In the nursing world, four domains should be empowered. Undergraduate and diploma programs can be integrated with courses. Graduate programs can be designed. Formal and informal continued educational programs for nurses on job and fellowships for PhD graduated nurses can be useful. Trying to make different groups of nurses ready for the ever-increasing speed of technology in the current century is possible, not only by parallel opportunities of learning, but also with the help of evaluating tools such as Self-assessment of nursing informatics competencies scale which can bring the same range of comprehention about informatics implementation ( Choi & Bakken, 2013). In conclusion, considering nursing outcomes takes advantage of information technology; educational arrangement is recommended to set short-term and long-term specialized courses focusing on the four target groups. Informatics courses for nursing students continued educational programs for registered nurses in work area, graduate programs at MSc and PhD levels for nurses and fellowship programs for doctoral graduates are recommended to be considered ( Figure 1 ).

It is essential that nurse educators incorporate the entire concept of informatics into the education of nurses.

Acknowledgements

It is hereby acknowledged colleagues and all those who assisted in conducting the study or critiquing the manuscript. The main enthusiasm of this study came out from the international symposium of telemedicine was held by Tehran University of Medical Sciences.

References

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Articles from Global Journal of Health Science are provided here courtesy of Canadian Center of Science and Education

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The Role of Nursing Informatics on Promoting Quality of Health Care and the Need for Appropriate Education

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In today's dynamic health systems, technology plays an important role in education and nursing work. So it seems necessary to study the role of nurses and highlight the need for appropriate information technology educational programs to integrate ...

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https://www.himss.org/resources/what-nursing-informatics

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We're often asked to explain what nursing informatics is. While it's difficult to quickly summarize this complex field, at its core, it takes the clinical and the technical languages of health and translates them into one. It also promotes meaningful, user-friendly and patient-centric innovation while driving improved outcomes for patients and enhanced clinical workflows for healthcare staff.

What is Nursing Informatics Definition?

The definition provided by the American Nurses Association (ANA) and recommended by HIMSS has become widely referenced in response to the question, "What is nursing informatics?"

Nursing informatics "is the specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage and communicate data, information, knowledge and wisdom in nursing practice."

As a global advisor and thought leader in healthcare information and technology, HIMSS has focused on this professional field for decades. Understanding the integral role this specialty plays today in the healthcare workforce, we've worked to amplify awareness by convening the experts and conducting extensive research on the topic.

What Are Nursing Informatics Roles and Requirements?

If you're looking to venture into this field of informatics, there are a variety of different skills and qualifications that can help ensure your success:

A bachelor's degree in nursing (BSN), at minimum

A master's degree and certifications, particularly for executive roles (chief nursing informatics officer)

Experience and/or strong understanding of working in a clinical setting

Strong technical skills; ability to quickly adapt to emerging technologies and innovation

Strong project management skills

An affinity for obtaining, analyzing and strategizing about data

Understanding of medical economics

Strong interpersonal skills

Leadership experience

Here are a few of the many job titles in use today:

Nurse informaticist

Nursing informatics specialist

Nursing informatics clinician

Clinical nurse informatics specialist

Chief nursing informatics officer

Perioperative informatics nurse

The field of nursing informatics has helped drive healthcare's application of technologies such as EMRs and computerized provider order entry. Nursing informatics professionals work with a diverse group of stakeholders across the care continuum, ultimately helping to bridge the gap between clinical and technical perspectives. Their number one priority is maintaining focus on patient safety.

This role was included in the top 15 highest paying nursing professions in the U.S. The HIMSS Nursing Informatics Workforce Survey, led by the HIMSS Nursing Informatics Committee, unveiled a multitude of findings about the profession:

Over two-thirds (68%) of the 1,359 survey respondents work for a hospital or multifacility health system

Ambulatory nurse informaticists are on the rise, with 6% of respondents reporting the ambulatory environment as their primary work place

The percentage of respondents (66%) who have a master's degree in nursing (24%), nursing informatics (27%), or some other field (11%) has gone up

The role of chief nursing informatics officer/senior nursing informatics officer continues to be on the rise with 41% of respondents reporting that their organization had the formal role

Overall, the results of the survey indicated that most professionals are burning bright with potential. After all, these findings confirm that many make higher wages than the average nursing professional, they enjoy the work they do, and many of them plan to keep doing it in the future.

What is Nursing Informatics Value?

" Nursing informatics specialists are the translators that have evolved into health tech innovators who establish businesses, manage medical economics, create technology and amplify the voice of end-user clinicians," said Danielle Siarri, MSN, RN, a HIMSS member and social media ambassador, global nursing expert and the lead publisher at InnoNurse. "Healthcare and technology were separate entities that have now fused into one language which evolves daily."

Nursing informatics professionals are unique, hybrid heroes of healthcare. As advocates for impactful innovation, their work keeps patients safe and at the center. Through their contributions and guidance, workflows are improved for healthcare staff and best practices followed in the effective management of information structures, processes and technology.

Through consistent leadership and hard work, the dynamic language and energy of health information and technology continues to grow and evolve along with the nursing informatics profession.

Empowering Nursing Informatics Professionals
National Nurses Week
HIMSS joins the American Nurses Association and the Alliance for Nursing Informatics and numerous other partners in celebrating National Nurses Week each May. The purpose of this week-long celebration is to raise awareness of the value of nursing and help educate the public about the role nurses play in meeting the healthcare needs of Americans. Join us in celebrating the nurses of our nation, who are at the forefront of improving patient care and transforming healthcare.
Online Journal of Nursing Informatics (OJNI)
Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the OJNI is a free, international, peer-reviewed publication that is published three times a year and supports all functional areas of the field. The journal was launched in 1996, with readership spanning over 49 countries.
Alliance for Nursing Informatics (ANI)
ANI represents thousands of nurses and brings together nursing informatics groups that function separately at local, regional, national and international levels. ANI is a collaboration of organizations that represent a unified voice for nursing informatics and provides the synergy and structure needed to advance the efforts of nursing informatics professionals in improving the delivery of patient care.
Identifying for a Unique Nurse Identifier
While nurses are making a difference in patients' health outcomes, without a nurse identifier, which is a unique numeric identifier automatically generated for each RN at the time of their state board exam, health systems have no way of demonstrating the full value of nursing.

It is a free, unique identifier, publicly available, that is automatically generated for each RN and LPN at the time of their NCLEX exam. No additional action is needed. The workgroup recommends that the NCSBN ID should be used by key stakeholders as the unique nurse identifier to enable identification and demonstration of the value of nurses through documentation in the EHR and other health IT systems.
Change Management Infographic
Formed within the HIMSS CNO-CNIO Vendor Roundtable, the infographic was created with a purpose to realize the value of the health IT investment, achieve benefits and sustainable transformation required for an organizational change management approach, as well as ongoing organization/vendor relationships for continued innovation for leaders across the healthcare spectrum.
CNIO Job Description
As the Chief Nursing Informatics Officer (CNIO) role varies within healthcare organizations, it is essential to have a standardized job description that can be referenced for needed competencies regardless of specific job title. This document provides recommendations for a C-Suite level CNIO job description including qualifications and Experience, Key Responsibilities, and Reporting Structure.
The TIGER Initiative
TIGER (Technology Informatics Guiding Education Reform) is a grassroots initiative focused on education reform, fostering interprofessional community development and global workforce development. The spirit of TIGER is to maximize the integration of technology and informatics into seamless practice, education and research resource development.
Register now Originally published May 14, 2019, updated February 27, 2023

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You asked, "What is nursing informatics?" And we answered. Explore the profession and how it is a driving force behind improved outcomes for patients and enhanced workflows for healthcare staff.

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https://pubmed.ncbi.nlm.nih.gov/28918322/

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doi: 10.1016/j.nedt.2017.07.014.

Affiliations

¹ School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK. Electronic address: S.OConnor@napier.ac.uk.

² Health Informatics Research Group, University AS Osnabrück, Osnabrück, Germany. Electronic address: U.Huebner@hs-osnabrueck.de.

³ HIMSS North America, TIGER Initiative, Chicago, IL, USA. Electronic address: tshaw@himss.org.

⁴ Omni Micro Systems und Omni Med Solutions GmbH, Hamburg, Germany and San Francisco, USA. Electronic address: shelblake@omnimicro.com.

⁵ Healthcare Informatics, Center for Computational Health, IBM Research, USA. Electronic address: Marionball@us.ibm.com.

Free article

Free article

doi: 10.1016/j.nedt.2017.07.014.

Affiliations

¹ School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK. Electronic address: S.OConnor@napier.ac.uk.

² Health Informatics Research Group, University AS Osnabrück, Osnabrück, Germany. Electronic address: U.Huebner@hs-osnabrueck.de.

³ HIMSS North America, TIGER Initiative, Chicago, IL, USA. Electronic address: tshaw@himss.org.

⁴ Omni Micro Systems und Omni Med Solutions GmbH, Hamburg, Germany and San Francisco, USA. Electronic address: shelblake@omnimicro.com.

⁵ Healthcare Informatics, Center for Computational Health, IBM Research, USA. Electronic address: Marionball@us.ibm.com.

Abstract

Information Technology (IT) continues to evolve and develop with electronic devices and systems becoming integral to healthcare in every country. This has led to an urgent need for all professions working in healthcare to be knowledgeable and skilled in informatics. The Technology Informatics Guiding Education Reform (TIGER) Initiative was established in 2006 in the United States to develop key areas of informatics in nursing. One of these was to integrate informatics competencies into nursing curricula and life-long learning. In 2009, TIGER developed an informatics competency framework which outlines numerous IT competencies required for professional practice and this work helped increase the emphasis of informatics in nursing education standards in the United States. In 2012, TIGER expanded to the international community to help synthesise informatics competencies for nurses and pool educational resources in health IT. This transition led to a new interprofessional, interdisciplinary approach, as health informatics education needs to expand to other clinical fields and beyond. In tandem, a European Union (EU) - United States (US) Collaboration on eHealth began a strand of work which focuses on developing the IT skills of the health workforce to ensure technology can be adopted and applied in healthcare. One initiative within this is the EU*US eHealth Work Project, which started in 2016 and is mapping the current structure and gaps in health IT skills and training needs globally. It aims to increase educational opportunities by developing a model for open and scalable access to eHealth training programmes. With this renewed initiative to incorporate informatics into the education and training of nurses and other health professionals globally, it is time for educators, researchers, practitioners and policy makers to join in and ROAR with TIGER.

Keywords: Community development; Competency; Curriculum; Education; Informatics; Learning; Nursing; Technology.

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Time for TIGER to ROAR! Technology Informatics Guiding Education Reform - PubMed

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Information Technology (IT) continues to evolve and develop with electronic devices and systems becoming integral to healthcare in every country. This has led to an urgent need for all professions working in healthcare to be knowledgeable and skilled in informatics. The Technology Informatics Guidin ...

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An Initiative for Technology and Health Informatics Education

The Technology Informatics Guiding Education Reform (TIGER) initiative from HIMSS provides the global health workforce with innovative informatics/eHealth tools and resources to transform health for all.

The global TIGER network collaborates to integrate informatics/eHealth into healthcare education, certification, practice and research through an inclusive, interdisciplinary, intergenerational approach.

Education Reform

We create an environment that maximizes the possibilities of technology toward knowledge development and dissemination, driving rapid implementation of best practices as well as competency and curriculum development.

Interprofessional Community Development and Growth

We actively engage and collaborate with the global healthcare workforce, students and adult learners to encourage standardized, person-centered and technology-enabled processes to facilitate teamwork and relationships across the continuum of care worldwide.

Global Workforce Development

We foster a respectful learning community of diverse stakeholders that embrace shared values to drive innovation and technology around the world.

Global Recommendations

While informatics recommendations on competencies and education support the design of curricula and courses, they are unable to inform educators on how to customize content for local needs and requirements. That's why the TIGER International Competency Synthesis Project (ICSP) and the EU*US eHealth Work Project came together to describe and validate the TIGER initiative's framework of global informatics core competencies for health professionals.

Together, these projects compiled a recommendation framework of health informatics competencies (v2.0) to measure, inform, educate and advance development of a skilled workforce throughout the EU and U.S. and around the world. Although the EU*US eHealth Work Project has ended, we are compiling global case studies to continue bringing our findings to life, with a focus on all states within the EU.
Hear TIGER International Task Force Former Co-Chairs Ursula Hübner, PhD, professor, University of Applied Sciences Osnabrück, Germany, and Beth L. Elias, PhD, MS, FHIMSS, adjunct faculty, Panuska College of Professional Studies, University of Scranton, present these important project findings at the HIMSS19 Global Health Conference.
TIGER International Task Force

The TIGER International Task Force, seated under the HIMSS Professional Development Committee, provides our global community of 34 countries with knowledge, leadership and guidance in its efforts to reform technology and informatics education. It provides domain expertise through activities, projects and collaborations within the interprofessional community to support faculty, students, adult learners and providers.

TIGER Resources

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The Technology Informatics Guiding Education Reform (TIGER) initiative offers tools and resources for learners to advance their technology and health informatics skills and for educators to develop valuable curricula.

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Nursing informatics is the specialty that integrates nursing science, computer science, and information science in identifying, collecting, processing, and managing data and information to support nursing practice, administration, education, research, and expansion of nursing knowledge.

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Informatics is how information is stored, retrieved, shared and used. It is not IT. IT is the device the information is kept on such as the computer itself.

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The Technology Informatics Guiding Education Reform (TIGER) Initiative is designed to address a set of skills that is needed by all nurses who will practice in the profession in the 21st century.

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TIGER identified core informatics competencies of health care professionals within the five domains: 1) nursing management 2) IT management in nursing (e.g. nurse informatics officer) 3) quality management 4) interprofessional coordination of care 5) clinical nursing.

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Interoperability in healthcare refers to timely and secure access, integration and use of electronic health data so that it can be used to optimize health outcomes for individuals and populations.

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https://dailynurse.com/the-importance-of-interoperability-in-nursing/

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Communication of all kinds is especially important in nursing. But this isn't limited to conversations between nurses and patients, family, or staff. Another crucial form of communication is interoperability-when different technologies can communicate with each other.

Susan Niemeier, MHA, BSN, RN, is the Chief Nursing Officer (CNO) with Ivenix, Inc., a new infusion pump company whose vision is to eliminate infusion-related patient harm. "I guide our overall clinical direction and oversee its marketing and product efforts, working closely with the research and development teams," Niemeier explains.

Niemeier, who has more than 25 years of experience in health care, took time to answer our questions about interoperability.
Please explain what interoperability is.
In its truest-and broadest-definition, interoperability is the ability to share information across multiple technologies. By ensuring that technologies work together, data is able to flow throughout and across the health system. When information flows freely and functions seamlessly, nurses are equipped to make smart decisions and achieve the best outcome for their patients. When done well, interoperability can lead to a new level of health care-a crucial step in optimizing quality of care.
How does interoperability matter in health care?
When health systems, devices, and applications seamlessly share information, nurses can make smart decisions about a patient's care. Having a holistic view of this information provides insights to make crucial-and sometimes lifesaving-clinical decisions and adjustments earlier in the treatment process.

Interoperability also brings together critical patient information (i.e., medical and medication history, comorbidities, lab evaluations, etc.) from various sources. Nurses do not have to synthesize the information themselves, which is inefficient, error-prone, and limiting. Instead, they can spend more of their time caring for patients.
What are the various products (including EHR) that are used in interoperability?
Devices that are traditionally known to be interoperable include electronic medical records (EMR), department systems (i.e., labs, radiology, pharmacies), specialty focus systems (e.g., cardiology, pulmonary, etc.) and ancillary systems (i.e., billing, insurance, etc.).

I'm passionate about the interoperability of medical devices with other systems. Medical devices contain real-time data that is powerful for nurses when making decisions for patients. Whether it's a patient monitoring device or a smart infusion pump, interoperable medical devices can elevate patient care practices.

Today's infusion pumps are an example of a medical device that is capable of interoperability, but has not yet realized its true potential. Currently, many infusion pumps rely on nurses to manually program data into the pump. This may lead to errors and ultimately, adverse drug events. In fact, of the estimated 1.5 million adverse drug events reported to the FDA each year, 54% are related to infusion-pump errors, and 61% of those events are serious or life-threatening.

According to a 2018 Health Information and Management Systems Society's (HIMSS) survey, 87% of HIMSS survey respondents considered interoperability as extremely or moderately important to what infusion pump they may use. A majority of respondents also considered the automation of programming and documentation efforts-facilitated by interoperability-extremely important for improving patient safety. Yet, less than 20% of respondents indicated actually having implemented EMR/infusion pump interoperability. Unfortunately, the greatest perceived barrier was projected cost with upgrading and connecting pumps with the EMR. We need to do a better job demonstrating that the cost is worth the effort in the form of patient safety, better outcomes, and overall efficiency.

Infusion pump interoperability enables auto-pump programming and documentation of clinical workflows. Automatically programming the pump from providers' orders reduces manual steps and variability in the medication administration process. Having the data flow seamlessly into the patient's medical record reduces time spent in documentation and potential for transcription and/or omission errors. Clinicians are empowered and ultimately satisfied knowing the right drug is administered in the right dose, to the right patient.

However, infusion interoperability can do more to optimize workflows and support smart decision making. Infusion pumps can communicate with alarm management systems to help prioritize alarms and eliminate nuisance interruptions. It can also unlock timely data to advance analytics, ultimately, build insight to enable high quality, safe care.

What roles do nurses play regarding interoperability? Why should nurses advocate for interoperability?

Imagine safer, simpler, and less stressful infusion delivery for nurses and their patients. To achieve this, the infusion pump should have a meaningful conversation with your EMR-as well as other clinical information systems.

When an infusion pump and EMR are interoperable, pharmacy-verified physician orders are transmitted directly from the patient record to the infusion pump-no need for error-prone, time-consuming manual input. Moreover, the pump can send accurate, time-stamped infusion data to the patient record, reducing clinician documentation time and the potential for errors.

This year, nurses are working through unprecedented times, dealing with a global pandemic. As an industry, those who answer the "call to care," need support from the technologies and devices they use regularly, including interoperable infusion pumps, to ensure they continue to improve care despite challenges.

Because of their important role in patient care, nurses have a critical voice in decision-making for technologies and medical devices. It is vital that decisions regarding interoperability include the voice of the nurse. Their perspective can shed light on what is really needed to help them be more effective in improving care and creating efficiencies.
Is there anything else you think is important for our readers to know?
It's critical for the health care community and industry to understand that interoperability is not a destination, it's a journey. We need to continue to work together to achieve seamless interoperability, especially with the help of nurses. Health technologies need to engage with and support nurses in decision-making based on the most comprehensive information. The 2018 HIMSS survey shared an estimated 70% of health systems plan to implement interoperable infusion pumps within the next four years. And, this is just one of the devices and systems that are being considered for interoperability use.

Ideally, we would already have these interoperable devices up and running, and integrated in every health system, we're not there yet. But I believe we're making great strides to ensure stronger patient safety practices and better care.

Related Content

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The Importance of Interoperability in Nursing - Daily Nurse

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Communication of all kinds is especially important in nursing. But this isn't limited to conversations between nurses and patients, family, or staff. Another crucial form of communication is interoperability-when different technologies can communicate with each other. Susan Niemeier, MHA, BSN, RN, is the Chief Nursing Officer (CNO) with Ivenix, Inc., a new infusion pump company whose vision is to eliminate infusion-related patient harm.

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https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.pdf

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Week 3 benefits of interoperability in healthcare

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Week 3 benefits of interoperability in healthcare

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https://oliveai.com/resources/blog/The-benefits-of-interoperability-in-healthcare

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The digitalization of healthcare promises great advances for global health, but it's not without its own challenges. The healthcare industry is overrun with health information technology (HIT) systems - EHRs (electronic health records), billing software, various portals for every payer and individual medical tools that each have their own interfaces, just to name a few. Many of these technologies can't work together due to siloed data and regulatory requirements, leaving healthcare providers and staff to do the manual work. And this administrative burden comes with a hefty price tag.

That's where interoperability can make an impact. Interoperability solutions in healthcare are the key to solving some of the industry's most challenging hurdles and promise to reduce the cost of care at an exponential rate.

What is healthcare interoperability?

First, let's start by defining interoperability in healthcare. Interoperability is the ability for various healthcare information technologies to share, interpret and use data cohesively, within and across healthcare organizations. It's critical for the industry, as it empowers patients to use and access their health information more easily and enables providers to deliver smarter, more efficient care. The challenge for healthcare organizations lies in how to achieve interoperability.

Why is interoperability important in healthcare?

According to a recent survey of 100 healthcare tech executives and 21 experts by Deloitte, value-based care and increasing regulation are the main drivers of healthcare interoperability today. But these aren't the only reasons interoperability is important. Health data has always been difficult to access and share due to security and patient privacy concerns. Yet the inability to access health data when it's needed can result in an incomplete understanding of a patient's health needs, negatively impacting patient outcomes and leading to higher costs. The ability of health systems to share critical patient data within and across hospital and healthcare system boundaries enables more effective care coordination and better patient outcomes.

The following are five leading benefits of interoperability solutions in healthcare:
1. Improving the quality of care and patient experience
Health systems aren't just embracing interoperability to achieve a regulatory requirement. As people live longer and value-based patient care becomes more important, interoperability and healthcare data sharing have become critical for improving patient outcomes and delivering effective healthcare. With access to real-time patient data, clinicians have a more holistic view of patients, can avoid medical errors, engage in, deliver seamless care collaborations and more. Essentially, interoperability in healthcare enables health systems to develop the technological infrastructure needed to maximize the value of their EHR data and deliver more comprehensive care.
2. Increasing healthcare efficiency
Healthcare data sharing between various health systems, payers and providers improves not only the quality of care but also the efficiency in which it's delivered. As the industry moves toward value-based care, interoperability solutions in healthcare are streamlining the overall coordination and delivery of patient care. For example, interoperability solutions in healthcare can eliminate time-consuming tasks like processing patient intake information, coordinating care between teams, updating patient records and even data aggregation for research. The result? Patients don't have to fill out the same forms over and over, medical research is accelerated and healthcare staff don't have to manually translate information from paper to EHR at every visit.
3. Reducing the cost of care
Today, one out of every three healthcare dollars is spent on administrative costs. The industry is already benefiting from the impact of automation on tasks that require time-consuming, error-prone manual work. And now health systems are using interoperability solutions to help reduce costs and medical errors by seamlessly sharing health data between providers, payers, labs and more. When interoperability solutions are scaled across a health system, patient outcomes are positively impacted, care delivery is streamlined and financial performance is amplified. Essentially, interoperability solutions in healthcare give clinicians the information they need to better coordinate care and reduce healthcare costs to patients - a win-win situation.
4. Mitigating physician burnout
Unlike in other industries where digital transformation has made work easier, healthcare EHR adoption has only exacerbated administrative and physician burnout. Growing requirements for regulatory compliance, lack of interoperability and the sheer number of software solutions have created additional work for clinicians and administrators alike. Reducing paperwork for staff and eliminating the need for manual data entry is a huge benefit of interoperability in healthcare. Interoperability solutions are seamlessly taking over labor-intensive tasks backlogged in EHR inboxes, minimizing the number of tasks that require manual touches, reducing redundant testing, and eliminating the burden placed on physicians by providers and payers by facilitating seamless health data exchange.
5. Protecting patient data security
Compliance is an important driving factor for healthcare interoperability and patients trust their providers to keep clinical data secure. As the number of cybersecurity attacks on healthcare organizations increases, hospitals are balancing the need for patient health data to be accessible with the need to maintain patient privacy. Today's healthcare interoperability solutions leverage HIPAA-compliant environments that ensure data is shared appropriately and securely - many top interoperability partners are even HITRUST-certified. The fewer healthcare workers who manually update patient data, the less chance there is of exposure to security threats.

How to achieve interoperability in healthcare

The growing adoption of new electronic data interoperability standards like HL7 FHIR is accelerating the development and the benefits of interoperability in healthcare. And as government mandates, increased regulations and the transition to value-based care continue to drive interoperability, both providers and payers are feeling the demand to enhance care delivery while decreasing costs. To achieve interoperability in healthcare, we must optimize electronic health information exchange services and tap into emerging technologies like artificial intelligence and machine learning to sift through the high volume of data.

Healthcare AI can act as an integrator for the workflows that bog down our healthcare system. With the power of artificial intelligence, health organizations can leverage a single integrator that works across different technology systems seamlessly to integrate data and provide interoperability. Imagine patients no longer having to carry their medical records from one office to another with full patient access, or providers having full access to a patient's medical history, wherever they are in the world - that's the goal of interoperability in healthcare.

Curious how you can drive interoperability across your healthcare system and beyond? Learn more about how Olive is making healthcare more connected by building the Internet of Healthcare.

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The benefits of interoperability in healthcare

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The digitalization of healthcare promises great advances for global health, but it's not without its own challenges. The healthcare industry is overrun with health information technology (HIT) systems - EHRs (electronic health records), billing software, various portals for every payer and individual medical tools that each have their own interfaces, just to name a few.

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Week 4 Data, information, knowledge, wisdom

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Week 4 Data, information, knowledge, wisdom

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https://ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/Columns/Informatics/Informatics-Evolution-of-Nelson-Model-Part-2.html

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Ramona Nelson, PhD, BC-RN, ANEF, FAAN
Citation: Nelson, R., (July 21, 2020) "Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2" OJIN: The Online Journal of Issues in Nursing Vol. 25, No. 3.

DOI: 10.3912/OJIN.Vol25No03InfoCol01
https://doi.org/10.3912/OJIN.Vol25No03InfoCol01

Nursing informatics is the nursing specialty that focuses on using the tools and processes inherent in automation to achieve the goals of nursing. Computers are excellent devices for managing the smallest of details, thereby supporting a safer, more efficient, and more effective quality healthcare system. At the same time, they are able to manage enormous databases of information and knowledge. But in the end - at the point of patient outcome - what is important is the ability to apply these tools in helping patients, consumers, families and communities.

One must always remember that the capabilities of computers (which are always changing) do not define the scope of practice for nursing informatics; rather, it is how nurses use these tools that define their practice. In turn, if we do not understand how nurses at all levels of knowledge and experience use computers to support their practice, we cannot design automated systems that truly support the goals of nursing.

The Data, Information, Knowledge and Wisdom Model (DIKW Model) provides a theoretical framework for defining the scope of practice for nursing informatics. In Part 1 of this two-part column, the driving forces for the creation of the DIKW Model were explained. Here in Part 2, the development of the model, and the evolution of the figures used to illustrate the model, are described. The figure used in this column, illustrating the model is still a 'work in process.' This column concludes by discussing limitations of the current figure and encouraging future leaders to develop a figure that more fully and effectively articulates the rich and complex relationships within the model, and in turn the scope of practice for nursing informatics.

Regarding the history of the DIKW Model and Related Figures, the initial attempt to articulate the concepts within the DIKW Model was a brief article defining the concepts of Data-Information-Knowledge-Wisdom ( Nelson & Joos, 1989). This publication did not include a model, but rather a table briefly explaining the concepts (See Table). The third column of the Table also identified the relationship between the concepts of data, information, knowledge, and wisdom and the various levels of computer systems.
Table. Terms, Meaning and Related Computer System
Over the next decade subsequent publications, for example, ( Joos, et al., 1996; Nelson, 2001) expanded on the four concepts, but did not create a figure showing the relationships between the concepts.

The first figure (See Figure 1) illustrating the model focused on the overlapping relationships between the concepts of data, information, knowledge and wisdom. The overlapping nature of the relationships between these concepts was depicted in the overlapping circles and in the words used to define the concepts. For example, the word organizing is included with both data and with information. This first version of the model ( Nelson, 2002) was published by Mosby, in a health informatics textbook ( Englebardt & Nelson, 2002).

Figure 1. Nelson Data to Wisdom Continuum.

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Used with the permission of Ramona Nelson, President Ramona Nelson Consulting at ramonanelson@verizon.net. Copyright 2002. All rights reserved.

At the time this textbook was written, Mosby was owned by Harcourt Brace & Company. In 2001, Reed Elsevier acquired Harcourt Brace & Company and Mosby then joined Elsevier. During this period, I was a member of the American Nurses Association (ANA) task force that had been charged with revising the 2002 ANA Scope and Standards of Practice for Nursing Informatics. In that role, I requested and received permission from Elsevier/Mosby for the ANA to print a modified version of Figure 1 that was subsequently titled Figure 2. Figure 2, as printed in this column, is a copy of that figure as it was printed in the 2002 ANA Scope and Standards for Nursing Informatics ( ANA, 2002), including the permission that was used in that publication..

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This second figure was an improvement on the first figure in that the interactions between and across the concepts was more effectively articulated. The double-headed arrow moving through each of the concepts signified that the flow of interactions moved throughout the continuum from data to wisdom. The oval with a dashed line labeled constant flux illustrated that this is an open system with constant interaction between the internal and external environment. In other words, context is important when using the model to understand specific phenomenon.

Shortly after the ANA publication, the copyright to the book was returned from Elsevier to the authors and I now own the copyright to these two figures as well as Figure 3, which was created in 2013. Since the copyright was returned, anyone who has made a request has been given permission to use any or all of the figures in publications, presentations and research free of charge. The only limitation is that permission to modify the figure is never granted and there is always a request to acknowledge the copyright. Many people have excellent ideas related to the model and how it may be used, but I believe permitting the publication of several different figures reflecting different interpretations of the DIKW model, all of which are identified as being the model, will create confusion - not clarity.

There have been publications that have included an inaccurate illustration of the model. The best source for ensuring you are viewing an accurate illustration of the model is the various editions of the ANA Standards of Practice for Nursing Informatics and my own publications.

While Figure 2 was a significant improvement, it still did not fully project the richness and complexity of the interactions within the model. It also did not appear to generate an appreciation for the importance of context as presented by the interaction within and between the internal and external environment. Figure 3 was developed and first published in a discussion of the theoretical foundations of health informatics presented in Chapter 2 of the textbook, Health Informatics: An Interprofessional Approach ( Nelson & Staggers, 2014).
Figure 3. Revised Nelson Data to Wisdom Continuum.
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Each revision of the model was created to more effectively illustrate the interacting relationships across the continuum and the inter- and intra-environmental factors that influence movement across and within the data to wisdom continuum. However, the current Figure depicting the Model continues to be limited by the reality of illustrating a four-dimensional phenomenon with a two-dimensional medium. Print, be it on screen or paper, is two dimensions. However, the Nelson DIKW continuum should first be conceptualized as an open living system with all the characteristics of an open living system. With such a system, time, the fourth dimension, is also part of the picture. So now, the question remains what would an automated three-dimensional model (maybe a hologram) look like, if it was fully functioning and changing over time?

Ramona Nelson, PhD, BC-RN, ANEF, FAAN
Email: ramonanelson@verizon.net

Dr. Nelson holds a baccalaureate degree in nursing from Duquesne University, and master's degrees in both nursing and information science, as well as a PhD in education, from the University of Pittsburgh. Prior to her current position as President of her own consulting company, she was Professor and Chair of the Department of Nursing at Slippery Rock University in Pennsylvania. In the early 1980's, one of Dr. Nelson's employee benefits, while teaching at the University of Pittsburgh, was the opportunity to take university courses for only $5.00 a credit. After taking courses in computer assisted instruction and theory for information science, she recognized these tools (computers) just might be somewhat useful at the bedside and in the classroom. She has been exploring and discovering just how useful they can be ever since.

Dr. Nelson's more recent research and publications focus on the specialty area of health informatics. Health Informatics: An Interprofessional Approach (2014), co-authored with Nancy Staggers, received the first place American Journal of Nursing Book of the Year award for Information Technology/Informatics. The second edition (2018) continues today as a primary textbook in the field of health informatics. Based on her contributions to nursing and health informatics she has been inducted into both the American Academy of Nursing and the first group of fellows in the National League for Nursing Academy of Nursing Education. She has also been recognized as a pioneer within the discipline by the American Medical Informatics Association. Her goal in serving as Editor of OJIN's Informatics Column is to open a discussion about how we as nurses in the inter-professional world of healthcare can maximize the advantages and manage the challenges that computerization brings to our practice.

American Nurses Association. (2002). Scope and standards of nursing informatics practice. Washington, D.C.: American Nurses Publishing.

Englebardt, S. & Nelson, R. (Eds.) (2002) Health care informatics: An interdisciplinary approach. St. Louis: Mosby-Year Book, Inc.

Joos, I., N. Whitman, M. Smith, & R. Nelson. (1996 ) Computers in small bytes: A computer workbook. 2nd edition. New York: National League for Nursing.

Nelson, R. (2001). Data processing. In V. Saba & K. McCormick (Eds.), Essential of Computers for Nurses (page 85-100). New York : McGraw Hill.

Nelson, R. (2002). Major theories supporting health care informatics. In S. Englebardt & R. Nelson (Eds), Health care informatics; An interdisciplinary approach (page 3-28). St. Louis: Mosby-Year Book, Inc.

Nelson, R, and Joos, I (1989). On language in nursing: From data to wisdom. Pennsylvania League for Nursing PLN Visions, 1(5), 6.

Nelson, R. & Staggers, N. (2014). Theoretical Foundations of Health Informatics., In R. Nelson & N. Staggers, (Eds.), Health Informatics: An Interprofessional Approach(page18-39). St Louis: Elsevier/Mosby.

© 2020 OJIN: The Online Journal of Issues in Nursing

Article published July 21, 2020

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Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2

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Ramona Nelson, PhD, BC-RN, ANEF, FAAN Citation: Nelson, R., (July 21, 2020) "Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 2" OJIN: The Online Journal of Issues in Nursing Vol. 25, No. 3. DOI: 10.3912/OJIN.Vol25No03InfoCol01 https://doi.org/10.3912/OJIN.Vol25No03InfoCol01 Nursing informatics is the nursing specialty that focuses on using the tools and processes inherent in automation to achieve the goals of nursing.

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Week 3 Interoperability and patient access fact sheet. 

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Week 3 Interoperability and patient access fact sheet. 

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https://www.cms.gov/newsroom/fact-sheets/interoperability-and-patient-access-fact-sheet

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Overview
The Interoperability and Patient Access final rule (CMS-9115-F) delivers on the Administration's promise to put patients first, giving them access to their health information when they need it most and in a way they can best use it. As part of the Trump Administration's MyHealthEData initiative, this final rule is focused on driving interoperability and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage (MA), Medicaid, CHIP, and Qualified Health Plan (QHP) issuers on the Federally-facilitated Exchanges (FFEs).

Lack of seamless data exchange in healthcare has historically detracted from patient care, leading to poor health outcomes, and higher costs. The CMS Interoperability and Patient Access final rule establishes policies that break down barriers in the nation's health system to enable better patient access to their health information, improve interoperability and unleash innovation, while reducing burden on payers and providers. Patients and their healthcare providers will have the opportunity to be more informed, which can lead to better care and improved patient outcomes, while at the same time reducing burden. In a future where data flows freely and securely between payers, providers, and patients, we can achieve truly coordinated care, improved health outcomes, and reduced costs.

Privacy, Security, and Standards
Ensuring the privacy and security of patient information is a top priority for CMS. Identifying the right standards can help data flow securely and efficiently. CMS, in partnership with the Office of the National Coordinator for Health Information Technology (ONC), has identified Health Level 7® (HL7) Fast Healthcare Interoperability Resources® (FHIR) Release 4.0.1 as the foundational standard to support data exchange via secure application programming interfaces (APIs). CMS is adopting the standards for FHIR-based APIs being finalized by HHS in the ONC 21 st Century Cures Act rule at 45 CFR 170.215. These requirements support the privacy and security of patient information.

CMS is taking additional steps to provide payers and patients opportunities and information to protect patient data and make informed decisions about sharing patient health information with third parties. For instance, as part of this final rule a payer may ask third-party application developers to attest to certain privacy provisions, such as whether their privacy policy specifies secondary data uses, and inform patients about those attestations. CMS is also working with payers to provide information they can use to educate patients about sharing their health information with third parties, and the role of federal partners like the Office for Civil Rights (OCR) and the Federal Trade Commission (FTC) in protecting their rights.

Patients have a right under HIPAA to access their health information. We believe they also have a right to know their health information is exchanged in a way that ensures their privacy and security. We are working to balance these important issues in a way that empowers patients to be in charge of their healthcare.

New Policies
This rule finalizes new policies that help liberate health information and move the healthcare system toward greater interoperability.

Provider Directory API: CMS-regulated payers noted above (except QHP issuers on the FFEs) are required by this rule to make provider directory information publicly available via a standards-based API. Making this information broadly available in this way will encourage innovation by allowing third-party application developers to access information so they can create services that help patients find providers for care and treatment, as well as help clinicians find other providers for care coordination, in the most user-friendly and intuitive ways possible. Making this information more widely accessible is also a driver for improving the quality, accuracy, and timeliness of this information. MA organizations, Medicaid and CHIP FFS programs, Medicaid managed care plans, and CHIP managed care entities are required to implement the Provider Directory API by January 1, 2021. QHP issuers on the FFEs are already required to make provider directory information available in a specified, machine-readable format.

To view the CMS Interoperability and Patient Access final rule, visit https://www.cms.gov/Regulations-and-Guidance/Guidance/Interoperability/index.

To view the ONC 21 st Century Cures Act final rule, visit, https://healthit.gov/curesrule

To receive more information about CMS's interoperability efforts, sign-up for listserv notifications, here: https://public.govdelivery.com/accounts/USCMS/subscriber/new?topic_id=USCMS_12443

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Interoperability and Patient Access Fact Sheet | CMS

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Overview The Interoperability and Patient Access final rule (CMS-9115-F) delivers on the Administration's promise to put patients first, giving them access to their health information when they need it most and in a way they can best use it.

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Week 4 transforming clinical data into wisdom

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Week 4 transforming clinical data into wisdom

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https://journals.lww.com/nursingmanagement/fulltext/2020/11000/transforming_clinical_data_into_wisdom__artificial.4.aspx

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Week 3 What is interoperability in healthcare?

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Week 3 What is interoperability in healthcare?

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https://www.boston-technology.com/blog/what-is-interoperability-in-healthcare

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As early as 1965, electronic health records were used to store and retrieve medical documents and other clinical information. Over the years, most healthcare providers have switched from paper to electronic health records, allowing the concept of interoperability to advance and grow.

In 2009, this was further reinforced when the American Recovery and Reinvestment Act was passed, allocating $19.2 billion for health information technology to help hospitals and physicians' offices implement the use of electronic health records in their day to day operations. As a result, healthcare organizations can now access and utilize various healthcare information applications to exchange, interpret, and use data cohesively through interoperability. But, what does this growth in interoperability of data mean for your healthcare organization? And what are the benefits of incorporating interoperability processes and solutions? Keep reading below to find out!

How does interoperability play a role in Healthcare?

Health data exchange architectures, application interfaces, and standards enable data to be accessed within and across organizational, regional, and national boundaries to provide seamless portability of information to optimize the health of as many populations as possible globally.

According to an article by the Healthcare Information and Management Systems Society (HIMSS), four interoperability levels are currently referred to within healthcare organizations. These levels include:

Foundational (Level 1):

Establishes the inter-connectivity requirements needed for one system or application to communicate data securely

Structural (Level 2):

Defines the format, syntax, and organization of data exchange, including at the data field level for interpretation

Semantic (Level 3):

Provides for standard underlying models and codification of the data, including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user

Organizational (Level 4):

Includes governance, policy, social, legal, and administrative considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities, and individuals. These components enable-shared consent, trust, and integrated end-user processes and workflows.

What is Health Information Exchange?

As mentioned above, interoperability is dependent on the exchange of information and data sharing. In this case, "Health Information Exchange" allows clinical information to be moved electronically to different healthcare information systems. This facilitates access to clinical data to provide safe, efficient, effective, and equitable patient-centered care. It can also be used by public health authorities to assist in analyzing the health of populations.

What are some of the ways interoperability improves Healthcare?

Improved patient care

Patients often do administrative tasks like searching for documents, filling out multiple forms, re-explaining their symptoms or medical history, and sorting out insurance. Using interoperability to streamline this process can help healthcare facilities give patients faster, more accurate, and coordinated treatment to enhance their overall experience.

Increased patient safety

According to a study conducted by Johns Hopkins, 44 percent of medical error deaths are preventable. By planning and implementing advanced interoperability processes, your organization can capture and interpret data across systems and applications, preventing errors resulting from missing or incomplete patient data.

Stronger privacy and security for patients

Interoperability can help enhance patient data privacy and security by requiring organizations to fully assess how their private health information is being used and shared. By analyzing how this information is entered into a secure, interoperable system, organizations can better understand where this data is located and who has access to it, helping them secure patient data and protect privacy.

Reduced healthcare costs

According to an estimate from the West Health Institute (WHI), system interoperability could save the U.S. healthcare system more than $30 billion a year. It can also improve care and hospital safety. As a result, interoperability allows organizations to save time with every patient by getting the right data to them, the physicians and caregivers, and the affiliate at the right time, every time.

So, how can BTC play a role in this process?

With demonstrable experience working with healthcare organizations to implement interoperability solutions, Boston Technology Corporation can work with your organization to ensure its interoperability processes are efficient, secure, and working optimally. To see how BTC can help you achieve this, click here to learn more.

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As early as 1965, electronic health records were used to store and retrieve medical documents and other clinical information. Over the years, most healthcare providers have switched from paper to electronic health records, allowing the concept of interoperability to advance and grow.

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Week 4 Data, information, knowledge, wisdom

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Week 4 Data, information, knowledge, wisdom

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The DIKW Pyramid represents the relationships between data, information, knowledge and wisdom. Each building block is a step towards a higher level - first comes data, then is information, next is knowledge and finally comes wisdom. Each step answers different questions about the initial data and adds value to it.

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Week 4 Applying the DIKW framework to evaluation of electronic health record system for nursing professionals.

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Week 4 Applying the DIKW framework to evaluation of electronic health record system for nursing professionals.

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https://www.himss.org/resources/applying-data-knowledge-information-wisdom-framework-usability-evaluation-electronic-health-record

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Abstract

The role of health informatics specialists, specifically nursing informaticists, has evolved over the past several decades with advances in health information technology. The American Nursing Informatics Association (2018) defines nursing informatics as a combination of "nursing science, information science, and computer science" (n.d.). Roles and responsibilities of nurse informatic specialists vary by facility but commonly involve areas of quality metrics, clinical documentation, and information technology support. Evolving roles due to the HITECH Act of 2009, Meaningful Use Incentive programs, and financial reimbursement systems that reward value-based care have increased the need for nursing informaticists (Nagle, Sermeus & Junger, 2017). As clinical leaders with technical and analytic skills, health informatics professionals have the ability to drive change within organizations wishing to improve quality of care for patients and reduce healthcare expenditures. Usability features of the hospital's electronic health record (EHR) system are critical to ensure clinicians are equipped with the necessary tools to provide safe and high-quality care.

This research project assessed the usability of a 54-bed hospital's electronic health record (EHR) to provide the organization with a snapshot of nursing staff satisfaction, efficiency, and effectiveness. The Data-Information-Knowledge-Wisdom framework was applied to study results and demonstrate the power of nursing perceptions to drive change within a healthcare organization. Efforts to improve patient safety and quality through usable EHR tools align with the goals of health care reform.

Introduction

Health Care Reform

The health care system in the United States has sought to reform issues related to access, cost, and quality for over three decades (Sultz & Young, 2014). The Triple Aim Initiative, initiated by the Institute for Healthcare Improvement (2017), seeks to "improve the patient experience of care (including quality and satisfaction); improve the health of populations, and reduce the per capita cost of healthcare" (p.1).

Key legislation passed to meet the goals of health care reform include the American Recovery and Reinvestment Act of 2009 and subsequent HITECH Act. Providing federally funded financial incentives for healthcare organizations to adopt electronic health records was an attempt to alleviate issues of cost and quality. Implementing health information technology aims to improve quality and cost by increasing transparency, improving communication between providers and facilities, and exchanging and storing patient data privately and securely. As of 2016, "over 95 percent of all eligible and Critical Access hospitals have demonstrated meaningful use of certified health information technology through participation in the Centers for Medicare & Medicaid Services (CMS) Electronic Health Record (EHR) Incentive Programs" (Office of the National Coordinator for Health Information Technology, 2017, p. 1).

These advances in health information technology are aimed at transforming care delivery processes and supporting patients and clinicians. Despite efforts of the Triple Aim to improve the health care system, clinician burnout and dissatisfaction with electronic medical records is a growing concern among healthcare leaders. Bodenheimer and Sinsky (2014) suggested adding a fourth component to the Triple Aim measures, which includes "the goal of improving the work life of health care providers" (p. 573). Additionally, Bodenheimer and Sinsky (2014) report clinician dissatisfaction with EHR use decreases the quality of care provided and increases patient safety risks, resulting in avoidable health care costs.

Usability Evaluations

Clinical end-user dissatisfaction has highlighted the need for usability evaluations of electronic health records. The International Organization for Standardization (2018) defines the three main components of usability as efficiency, effectiveness, and satisfaction. New literature is exploring the relationship between the emotional experiences of the end-user, in addition to the historical components (Bevan, Carter, & Harker, 2015). Incorporating usability principles into the design and development of clinical software applications is imperative to promote patient safety and quality enhancements, as dissatisfaction and poor usability are associated with errors that compromise patient safety (Middleton et al., 2013).

John Brooke developed the System Usability Scale (SUS) in 1986. As a questionnaire-based survey providing a global snapshot of the efficiency, effectiveness, and satisfaction rates of a system, it is one of the most widely used evaluation tools (Usability.gov, 2013). SUS is beneficial for measuring usability due to its easy distribution to participants, and quick analysis of results. The survey is comprised of ten questions related to software use. Participants rate the questions on a scale ranging from "strongly agree" to "strongly disagree." Results are then analyzed using Brooke's formulated calculation, which involves converting scores into a new number and determining a composite score. SUS scores range from 0-100, with the 68th percentile considered an "average" score (Usability.gov, 2013).

Moody, Slocumb, Berg, & Jackson (2004) developed another useful usability evaluation tool. The usability assessment survey sought to assess the perception of nurses on the effects of electronic health record systems on quality and safety of patient care. This assessment is still relevant today, as widespread adoption of EHRs has highlighted the need for evaluating the usability of electronic tools used daily to assist nursing staff in caring for the patient. Nurses represent the largest group of EHR users, utilizing electronic tools to record and analyze pertinent health data (Rojas & Seckman, 2014). Designing clinical applications that meet the usability principles of efficiency, effectiveness, and satisfaction for nursing professionals supports the potential to drive change in a healthcare industry seeking methods to improve quality and safety for patients.

Among the many types of usability evaluation tools, surveys remain widely popular due to easy distribution and quick analyzation of results. For this research study, an electronic survey was selected as the usability evaluation tool for this study. The current EHR has been in place for over three years. Nurses utilize EHR software throughout their assigned shift to access pertinent patient information, document results, and communicate with members of the multi-disciplinary care team. Results of this study sought to contribute to the broader knowledge base of health informatics, providing the perception of nursing professionals on the usability features of electronic tools utilized to provide direct patient care on a daily basis. As stated by Bodenheimer and Sinsky (2014), equipping health care professionals with the appropriate electronic tools is key to the success of improving quality of care for patients, patient safety, and reducing health care expenditures. Results of this study will provide evidence for nursing administration at the performance site to consider workflow improvement strategies, or software enhancement requests for vendor development. As the most substantial body of health care professionals, nurses are increasingly respected for their influence on improving quality and patient safety initiatives (Kennedy, 2018).

Data-Knowledge-Information-Wisdom Framework

The American Nurses Association has adopted the Data-Information-Knowledge-Wisdom framework as an integral component of informatics nursing (Topaz, 2013). In its simplest form, the data obtained from this usability evaluation are the perceptions of usability by nursing professionals. This data is turned into information by combining "data + meaning" (Topaz, 2013). Knowledge is the result of understanding the implications of research information, and lastly, wisdom is the application of knowledge to improve healthcare processes for patients. Applying this framework to the Usability Evaluation of Electronic Health Record System for Nursing Professionals allows nursing perceptions about the impact of EHRs on patient care to influence reform efforts on quality and safety initiatives for patients. Understanding the usability of electronic health tools is critical to developing and maintaining health information technology that will assist clinicians in delivering high quality, cost-effective care.

Methods

Study Site

Research study participants included full-time, part-time, and PRN (pro re nata or "as needed") nursing professionals at a 54-bed specialty hospital in south-central Kansas. Eligible participants utilized the hospital's EHR system on a daily basis to perform necessary job activities. Nurses working in a unit that does not utilize a electronic health record for documentation or patient care activities were ineligible to participate in the study.

The study site location has utilized electronic nursing documentation for over five years and implemented a computerized physician order entry (CPOE) system in 2015. Nursing staff utilize health information technology on a daily basis to access patient information, communicate with members of the multi-disciplinary care team, and coordinate patient services. The Usability Evaluation of Electronic Health Record System for Nursing Professionals was used to gather nursing perceptions on the efficiency, effectiveness, and satisfaction of the hospital's EHR, to provide insight on process improvement and potential product enhancement requests for nursing administration.

Internal Review Board

After receiving clearance from the study site location, a research protocol was developed and submitted to the University of Cincinnati Internal Review Board, in accordance with federal regulations. Required documentation was submitted, including the recruitment letter, information statement, research study protocol, IRB application, and study site letter of support. Additionally, the student and faculty advisor completed required CITI training for research studies conducted through the University of Cincinnati. The student completed requested revisions by IRB staff to the research protocol. The revised protocol was submitted and authorized under expedited IRB.

Developing a Usability Evaluation

The Usability Evaluation of Electronic Health Record System for Nursing Professionals is comprised of three content areas: Brooke's System Usability Scale (2013), Moody et al.'s (2004) Usability Assessment, and open-ended questions related to the positive and negative aspects of the hospital's electronic health record. This mixed methods study utilized quantitative and qualitative data to gather nursing perceptions on the usability features of the EHR, provide insight to process improvement strategies, potential product enhancements, and expand the knowledge base of health informatics.

System Usability Scale

John Brooke developed the System Usability Scale in 1986. It is one of the most widely used tools to assess the usability features of hardware or software applications. This "quick and dirty" assessment tool seeks to determine if the software utilized by nursing professionals meets the demands of daily job activities. The scale is comprised of the following ten questions, with responses rated on a Likert Scale from "Strongly Agree" to "Strongly Disagree" (Usability.gov, 2013):

1. I think that I would like to use this system frequently.

2. I found the system unnecessarily complex.

3. I thought the system was easy to use.

4. I think that I would need the support of a technical person to be able to use this system.

5. I found the various functions in this system were well integrated.

6. I thought there was too much inconsistency in this system.

7. I would imagine that most people would learn to use this system very quickly.

8. I found the system very cumbersome to use.

9. I felt very confident using the system.

10. I needed to learn a lot of things before I could get going with this system.

Interpretation of SUS scores is accomplished by calculating a percentile composite ranking. Brooke (Usability.gov, 2013) defines the SUS calculation as follows:

To calculate the SUS score, first sum the score contributions from each item. Each item's score contribution will range from 0 to 4. For items 1,3,5,7,and 9 the score contribution is the scale position minus 1. For items 2,4,6,8 and 10, the contribution is 5 minus the scale position. Multiply the sum of the scores by 2.5 to obtain the overall value of SU.

The final composite ranking is a percentile of System Usability. The average score is 68, with numbers above 68 representing above-average usability; and scores below 68 representing below average usability. Brooke (1986) stated that the System Usability scale is easy to administer, analyze, and providew tangible results on the usability features of an electronic system. While it can be used alone, incorporating subjective questions into a usability assessment survey is beneficial in obtaining a well-rounded picture of system usability.

Moody Usability Assessment Survey

Moody et al., (2004) developed the Usability Assessment Survey to determine "user satisfaction with the functionality of the current system, perceived problems, barriers, and frustrations associated with the current EHR documentation system, and attitudes in general toward the use of an EHR" (Background, para. 2). This usability survey was first administered to 100 nursing staff at a magnet hospital in Florida. The survey consisted of demographics, access and location of hardware used, and a Likert Scale of nursing perceptions related to electronic nursing documentation.

For the purpose of this capstone research study, the following questions were deemed appropriate for the usability evaluation at the study site location:

1. Use of electronic health records is more of a help than a hindrance to patient care

2. Use of computerized charting has helped to improve documentation of the clinical record

3. Computerized charting has decreased the workload of nurses and other personnel

4. In time, the use of electronic health records will lead to improved patient care

Questions related to the location of hardware were omitted, since the study site location had previously addressed these issues. Descriptive statistics were used to analyze usability assessment survey results, including the correlation between demographics and nursing perceptions of the EHR.

Open-Ended Questions of Nursing Perceptions

Nursing professionals were asked two opened ended questions:

1. List the positive aspects of the hospital's EHR

2. List the negative aspects of the hospital's EHR.

Content analysis was performed to interpret results of these open-ended questions.

Data Analysis

Survey results were exported from the online survey site to a CVS file and imported into Microsoft Excel. In order to analyze the System Usability Scale results, data cleaning was performed by assigning numerical values to categorical data. After data cleaning, the SUS scores were determined based on John Brooke's calculation guide (Usability.gov, 2013).

The average SUS score among all participants was calculated, along with the average SUS scores among different demographics. This included the average SUS score of participants based on level of education, employment status, and years of nursing experience.
Questions from Moody et al.'s (2004) Usability Assessment Survey were also exported to Microsoft Excel for data analysis. Participant responses to each question were summarized, and demographic relationships and patterns were explored. Responses based on level of education, employment status, and years of nursing experience were compared and contrasted.

Finally, the pen-ended question responses related to the positive and negative aspects of the hospital's EHR were imported into Microsoft Excel for analysis. Content analysis tallied common responses from participants.

Consent Process

Documentation of consent was waived by the Internal Review Board at the University of Cincinnati, due to the minimal risk presented to research participants in an electronic survey. Study site human resource personnel distributed a recruitment letter and information statement to eligible nursing professionals prior to completing the survey. Contact information for the primary investigator and IRB at UC was available to potential research participants, if they wished to discuss any questions or concerns related to the study.

Results

Demographics

The Usability Evaluation of Electronic Health Records for Nursing Professionals was electronically delivered to 100 nursing personnel. Twenty-eight nurses participated in the study. The majority of participants held a bachelor's degree (64%), followed by an associate degree (29%), and a master's degree (7%). The majority of nursing participants were employed full-time (68%), five worked part-time and five were PRN employees. Participant nursing experience ranged from 1 to 51 years, with an average of 22.32 years of nursing service.

Brooke's System Usability Scale

The average SUS score among the twenty-eight participants was 44.9. This SUS score is considered low, since John Brooke defined the average SUS score of 68 to be an indicator of good system usability. The highest SUS scores were reported by participants with a bachelor's degree, with an average SUS score of 49.3.

Moody's System Usability Assessment

Fifty-five percent of participants agreed the "use of electronic health records is more of a help than a hindrance to patient care." Full-time employees were more likely to find electronic health records helpful to patient care. Analyzing respondents' level of education found no difference of opinion among participants with a bachelor's versus an associate degree. Half of each of these groups of respondents said they "agree" or "strongly agree" with the statement.

Fifty-three percent of participants agreed to the statement "the use of computerized charting has helped improve documentation of clinical records." There was no noticable variance in participant responses based on level of education or employment status.

Sixty percent of all nurses surveyed reported "disagree" and "strongly disagree" that computerized charting has decreased the workload of nurses and other personnel. The majority of participants disagreed with this statement, regardless of demographics. Sixty-three of full-time employees disagreed that computerized charting has decreased the workload of nurses and other personnel. Interestingly, fifty-six percent of nurses with less than ten years of experience neither agreed nor disagreed that computerized charting had decreased the workload of nurses and other personnel. However, seventy-nine percent of nurses with greater than ten years of experience disagreed with the statement.

Forty-eight percent of nurses surveyed agreed that electronic health records would lead to improved patient care, while forty percent of participants were neutral. Forty-three percent of full-time employees agreed, while a separate forty-three percent of full-time employees neither agreed nor disagreed. Level of education did not influence participant response; however, sixty-seven percent of nurses with less than ten years of experience agreed that electronic health records would lead to improved patient care. Alternatively, only thirty-eight percent of nurses with greater than 30 years of experience agreed that EHRs would improve patient care.

Positive Aspects of EHR

Nursing professionals were asked to list the positive aspects of the hospital's electronic health record. Sixty-one percent of participants responded to positive aspects of the EHR. Thirty-five percent of participants listed the ease to access patient information, including documentation on previous admissions and test results as a positive component of the hospital's EHR. Eighteen percent of participants referenced documentation legibility as a benefit. (See Table 1).

Negative Aspects of EHR

Nursing professionals were asked to list the negative aspects of the hospital's EHR. Seventy-one percent of participants listed the following undesirable characteristics of the hospital's software (See Table 2). Twenty-five percent of participants listed "cumbersome," and fifteen percent of participants listed "slow." 45% of participants referred to "time" as an issue, whether referring to the number of computer clicks to complete nursing documentation, time to access pertinent patient information, or the amount of time computerized documentation reduces time with the patient.

Discussion: Application of the Data- Information-Knowledge-Wisdom Framework

Data

The Data-Knowledge-Information-Wisdom framework applies to the Usability Evaluation of Electronic Health Records for Nursing Professionals. Research study participants responded to questions related to John Brooke's System Usability Scale, Moody et al.'s Usability Assessment Survey, and open-ended questions related to the positive and negative aspects of the hospital's EHR. These questions produced an enormous amount of data, however this data is futile without analysis, interpretation, and application to clinical practice.

Information

Data transformation into information occurs with the meaningful analysis of study results. This usability evaluation of nursing professionals found nurses at the study site reported low system usability of the hospital's EHR. Additionally, nursing professionals believed that EHRs hinder patient care and increase the workload of nursing staff. However, nursing professionals at the study site also reported that EHRs have improved clinical documentation and will eventually lead to improved patient care. Information from this research study can be compared to previous usability evaluations to generate knowledge.

Knowledge

Understanding the relationships between research study information and the various data patterns produces knowledge of usability evaluation outcomes. The study site results showed a low SUS score of 49.3%. A comparison was made with results from a previous study conducted at six different hospitals with 1879 particiapants that reported SUS scores of 56.9, 60.1, 60.5, 63.3, 52.2, and 58.3 (Cho, Kim, Choi, & Staggers, 2016).

Comparing the nursing perceptions of this research study with Moody et al.'s (2004) results, this study site reported lower agreement statements. Only 50% of nurses at the study site agreed that EHRs are more of a help than a hindrance to patient care, compared to 81% in Moody's study. Additionally, only 53% of nurses agreed that EHRs have improved documentation, unparalleled to 75% in Moody's research. 18% of nurses surveyed reported EHRs have decreased the workload of nursing personnel, whereas 36% of participants in Moody's study agreed. 48% of nurses agreed EHRs will lead to improved patient care, while 76% of nurses surveyed by Moody agreed.

Considerations to the variance in these reports are the type of facility surveyed, number of research participants, and different software programs utilized by each health care organization. Moody et al.'s (2004) study also took place prior to Meaningful Use incentive programs, which increased the usage of electronic health records. Changes in reporting requirements and mandated electronic documentation may have also influenced the perception of nursing professionals.

Wisdom

The wisdom component of this framework is achieved when nursing leadership utilizes the knowledge gained from this research study and applies it to clinical practice to improve the usability features of the hospital's electronic health record. This may include further investigation into low SUS scores, as 49.3% falls considerably below John Brooke's average SUS score of 68. Submitting negative aspects of the hospital's EHR as enhancement requests to the software vendor for future product development, such as accessing pertinent medical information and reducing the number of clicks required to complete documentation would acknowledge issues reported by nursing staff. Additionally, modifying clinical workflows to improve time spent with the patient versus time spent on the computer would address concerns described by survey participants.

Previous studies have sought to gather nursing perceptions on the impact of computerized documentation on patient care. This research study intended to gather information on how EHR usability affects patient care, by utilizing John Brooke's System Usability Scale and Moody's System Usability Assessment. Ongoing research is necessary to ensure nursing professionals are adequately equipped with the appropriate electronic tools to deliver high quality care.

Conclusion

Usability features of electronic health records are a critical component of health care reform. The widespread adoption of EHRs was intended to address long-standing issues related to cost, quality, and access to health care services in the United States. As Bodenheimer and Sinsky (2014) stated, these overarching goals of health care reform cannot be achieved without providing clinicians with adequate health information technology to address these challenges. As this research study found, nurses at this study site location reported a low SUS score and found that electronic documentation has taken time away from patient care activities. Study results reinforced the need for clinical insights into the design, build, implementation, and ongoing maintenance of electronic health records. This is essential to avoid clinical dissatisfaction and burnout while improving the quality of patient care and patient safety.

Citation: Nation, J., & Wangia-Anderson, V. (Winter 2019). Applying the Data-Knowledge-Information-Wisdom framework to a Usability Evaluation of Electronic Health Record System for Nursing Professionals. Online Journal of Nursing Informatics (OJNI), 23(1). Available at http://www.himss.org/ojni
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
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Applying the Data-Knowledge-Information-Wisdom framework to a Usability Evaluation of Electronic Health Record System for Nursing Professionals

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The role of health informatics specialists, specifically nursing informaticists, has evolved over the past several decades with advances in health information technology. The American Nursing Informatics Association (2018) defines nursing informatics as a combination of "nursing science, information science, and computer science" (n.d.).

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Week 4 Use of management systems in nursing workflow.

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Week 4 Use of management systems in nursing workflow.

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839280/

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Abstract

Nurses are at the forefront of providing healthcare services to individuals of all age groups and with varying medical conditions. Aside from the critical knowledge and technical skills from nursing science, advancement in technology has assisted nurses in delivering quality nursing care by streamlining workflow processes and ensuring that data can easily be retrieved or modified. Electronic health records dramatically changed the landscape of the healthcare practice by providing an electronic means to store data and for healthcare professionals to retrieve and manipulate health information in a secured and collaborative environment. But with the nature of data being stored in the electronic health records, nurses still need to organize and process these data into relevant information, knowledge or wisdom so they can provide better holistic care to patients. This discussion paper details the role of content management systems in addressing nursing workflow by providing a mechanism for nurses to be developers themselves, and not just users or consumers of health innovative technologies. By using content management systems as platform for application development, nurses or other healthcare professionals, may be able to address problems with internal workflow without having to incur huge amounts in software development, or having to extensively learn programming languages.

Keywords: Content management system, Electronic health records, Software, Health personnel, Nursing informatics, Programming languages, Workflow
What is known?

* Data management is a crucial aspect of healthcare innovations. Clinical data provide a holistic and collaborative understanding of the clinical condition, and the management of patient care.

* Implementations and deployments of electronic medical records have been vast, particularly in developed countries. The electronic medical record, however, can be thought of as a repository of patient data. Human cognitive perception is still needed to provide meaning to the data stored in the electronic records.

What is new?
* Content management systems can be used when storing synthesized data based on human understanding. Focusing on transforming these stored electronic clinical data into information, knowledge and wisdom is part of data utilization to provide safe and effective patient care.

1. Nurse and the EHR

The evolution of the profession of nursing does not only depend on how nursing as a science evolves, but how interrelated or interdependent fields of study progress as well. Nurses owe their knowledge in pathology and physiology from discoveries made by scientists in the medical sciences, and from nurses engaged in the field of pathophysiological research, as with those nurse scientists working in the areas of symptoms management, diabetes management and genetic research. The expansion in the knowledgebase in computer, information and cognitive sciences paved the way for nursing informatics to be realized as a crucial field for nurses, either as fundamental tool for beginning nurses or as a specialty field for nurse informaticians.

The electronic health record (EHR) is a product of interdepartmental and interdisciplinary collaboration to address a common concern in the healthcare industry - how to store, retrieve and manipulate patient data in a secured and collaborative environment. There are glaring differences between fully implementing a basic EHR compared to no implementation or not fully implemented. In a setting where there is no basic EHR or if the EHR is not fully implemented, vital patient information tend to get lost during change of shift reports. There is greater possibility that patient safety and quality of care could be compromised. Nurses were also found not confident enough in assessing patient's readiness for discharge. In places where there is full implementation of basic EHR, there was enhanced or more efficient nursing care, and better coordination of care and patient safety [ 1].

Aside from storing clinical information for later retrieval, current electronic medical records have add-on features. An integrated clinical decision support system (CDSS) based on intuitive data entry forms could allow data input parameters to be patterned based on the CDSS architecture to address both acute and chronic health conditions [ 2]. Patient access to personal health record (PHR) is crucial, and some EHR integrate PHR within their system. Among the three configurations of a PHR - standalone, tethered, integrated - researchers have found that only the integrated approach has the true transformative potential when it comes to patients being able to manage their own health. Having a PHR module within the EHR improves the quality and portability of patient information; enables real-time communication between patient and provider; and improves patient knowledge on health [ 3]. Content templating, such as physician progress and unit reports, may be available in some EHR's. These templating modules assist healthcare providers in accessing pertinent information that is vital to provision of patient care.

The impact of EHR integration in the hospital system is a significant one, particularly to nurses caring for patients at the bedside. There was a change on how nurses spent time in certain activities, such as the amount of time interacting with computers; though the impact was uniformly dispersed among other activities. The duration and frequency of activities done by nurses were not adversely changed by the introduction of additional healthcare innovations. There was also no significant change in the time spent by nurses with their patients, verbally interacting with them, as the nurses try to integrate the change in workflow to accommodate health information systems in their normal routine [ 4]. This says a great deal about the flexibility of nurses when it comes to adapting to changes in the workflow system. By balancing their use of computers and patient care, nurses could reap the benefit of the integration of health information technology into the field of healthcare.

The design of clinical systems should address particular workflow and must be done on a continuous and iterative approach based on identified needs. A crucial module in any electronic health record system is the computerized provider order entry or CPOE. Benefits of CPOE include readable and understandable physician orders, the ability of systems to be accessed remotely, and reduced order turnaround times [ 5]. A study conducted revealed the CPOE can affect clinical work. It can cause interaction difficulties between computer systems and users. It can cause changes in the dynamics of clinical workflow. It may also address only a subcomponent of the totality of the workflow for various clinical personnel. CPOE modules may diminish awareness of the clinical situation. And lastly, in terms of organizational operation, a poorly designed CPOE could ultimately mirror the quality of internal policies and procedures [ 6].

Health information systems (HIS) are information technology systems applied to healthcare with four crucial functions: (1) to generate data, (2) to compile or aggregate data, (3) to analyze and synthesize data, and (4) to communicate and use data [ 7]. Healthcare institutions use HIS's in gathering, synthesizing and storing data, and in the exchange of information in the decision-making phase at various sectors of healthcare service to promote quality [ 8]. These systems are built to streamline the workflow processes in the hospital or any healthcare setting.

A crucial element in the success of HIS implementation is acceptance by users, particularly nurses. No matter how advanced the system is or its associated modules or peripherals, if there is poor acceptance by users, problems in usability can occur. Education and training play a vital role in introducing new information systems to frontline personnel. Before, during and after HIS go-live implementation, nurse informaticians have the role of assisting nurses assimilate into the new workflow environment, and to gather necessary feedbacks to help improve the overall EHR experience. Time is also an important element when it comes to acceptance. A study revealed that nurses in the intensive care unit were more accepting of the EHR system at twelve (12) months compared to three (3) months after EHR implementation [ 9].

The integration of information technology innovations in healthcare does not only bring benefits to the organization. Sometimes, a change in the status quo could also usher in disruptive change on the negative side. Though nurses perceived the retrievability of data as one of the strengths of the electronic medical record, other nurses observed the lack of relevance in documentation as one of the barriers [ 10]. A field study of hospitals in Saudi Arabia highlighted six (6) main categories of barriers to the implementation of health information systems: human, professional, technical, organizational, financial, and legal or regulatory barriers. Of the six identified main categories, human and financial barriers are the two prominent categories of barriers to successful EMR implementation [ 11]. Before deploying any EMR system, the implementing institution must assess the readiness of its personnel and the impact the system will have in the different healthcare professionals. If the overall cost of EMR deployment is burdensome for the organization, if it does not have the determination and concrete policies on deploying an EMR system, or if it does not have the technical capabilities to implement the system, it is unlikely that the organization would deploy an EMR system. Due diligence must be exercised to make sure the chosen EMR system adheres to laws and implementing rules and regulations of a particular state or country. No matter how advanced the EMR system is, if it does not follow legal or regulatory requirements, the implementing institution may choose to delay deployment of an EMR system, or make the necessary modifications to the system - that could cost more.

Gathering feedback from users of health information systems is a crucial part of health information technology implementation. The integration of electronic medical records in the workflow of nurses may cause disturbance in daily routine. It is imperative that nurses be included in the design of health information systems since nurses form bulk of the clinical personnel in the hospital, and any disruption in the nursing workflow could ultimately affect individualized care and patient safety. It has been shown that the implementation of health information systems has a negative impact on nurses when the system is not adequately designed. Nurses should be involved in software design aspect so that the core and intricacy of nursing does not disappear in the system architecture [ 12, 13].

To highlight the importance of gathering feedbacks from or including nurses in the software development process, a technology company in Hong Kong devised an electronic nursing information system for use by nurses in the operating theater. The information system was designed using free and open source software components. This means the source code is readily available, and can have features extended based on the needs of nurses. The operating theater information system utilized Joomla as the content management system (CMS). The Joomla system designed used MySQL for database management, PHP as web application programming language, and Linux as operating system. The main objective of the electronic nursing information system is to eliminate human error in documenting instrument count related to illegible handwriting or records that are unaccounted for [ 14]. This nursing information system deployment clearly illustrates the potential of CMSs to enhance nursing clinical workflow.

2. Data-information-knowledge-wisdom Continuum

Information management forms a vital part of the nursing workflow. Even before the introduction of health information technology innovations, such as the electronic medical records, nurses have been keeping record of care rendered to patients, clinical assessments, procedures done and medications administered through chart entries. A summary of patient care can also be found in the nursing kardex which is a tool used by nurses to condense healthcare information necessary for patient care. The need to organize and synthesize data to produce relevant information or knowledge is crucial in the nurse's decision making process.

The Data-Information-Knowledge-Wisdom (DIKW) Continuum is a concept of synthesizing data to have meaningful information to produce the knowledge necessary to have the right wisdom to make informed clinical decisions with regards to patient care. Data is the most fundamental and discrete element of the DIKW model, and is produced either through manual or automated methods of observation. Information happens when meaning is attached to organized and processed data, and is created by integrating various data points to form a meaningful depiction of the situation. When information is further organized and processed, knowledge is produced. Knowledge is a body of meaningful information composed of discrete data so that relations and interactions are defined and operationalized, and is generally influenced by assumptions and norms of a particular field of practice. When the nurse has the sufficient knowledgebase, the nurse can now use this knowledge in making clinical decisions with regards to patient care. Wisdom is the utilization of the knowledge acquired from meaningful information to clinical judgment, and is related to the nurse's capacity to make informed clinical judgment based on the knowledge the nurse has accumulated [[15], [16], [17]] (see ).

One of the important features of most electronic medical records is the ability to layout patient care summary based on information stored in the database. Assessment data, such as blood pressure, heart rate and temperature, can be further combined with medication administration data, such as the medication name and dosage, to build information and knowledge on how an individual patient's body would react to a particular drug at a specified dose. This could then aid the physician in developing wisdom on how to manage the patient's blood pressure on an individualized basis. For nurses, this could provide wisdom to reinforce the need for evaluating the patient before and after intervention, since even though the blood pressure and heart rate can be affected by anti-hypertensive medications, the nurse must first assess whether changes in heart rate from baseline could have been caused by an increase in body temperature [ 18, 19].

The patient care summary is generally an electronic template where needed patient information is summarized. An electronic patient care summary is similar to the nursing kardex where nurses can browse through patient information without having to go through modules or sections of the health information system. Templating of patient care information or summary is a process of transforming data stored in the electronic medical record into relevant information or knowledge needed to provide quality nursing care. However, in order for the patient care summary template or module to be useful to nurses, the template and its content should be relevant to the workflow of nurses. If the summary does not provide enough cognitive support to nurses - as with templates designed solely for physicians, or if the layout is inadequate, inflexible and does not display evident information, or assist nurses encode information, the chances of the patient summary being used in actual clinical setting would be slim [ 20].

Another area where nurses could benefit from electronic templating is shift reports or bedside rounds. The exchange of information from the ongoing to the incoming shift is vital in providing continuity of patient care. Reports should not be lengthy narratives, but be concise and should capture the overall patient condition - outlining relevant events that took place and plan of care. The Situation-Background-Assessment-Recommendation (SBAR) protocol is a format that nurses use to present information when conducting bedside rounds. Using this method, nurses are able to provide a more directed and efficient way of communication. With a standardized electronic template tailored for nursing workflow, there is reduction in time spent for change-of-shift reports, decreased end-of-shift overtime, and a more homogenized process of information exchange. There is also enhancement in the quality of information and fulfillment with the process among staff members. The use of electronic templating or information layout is an integral processes of synthesizing data from the EHR. Nurses working at the bedside are in a strategic position to influence change that could benefit hospital operations and clinical practice [ 21, 22].

The institution may decide to adopt an interdisciplinary or interdepartmental approach in preparing an electronic template module for patient care summary based on the available features of the electronic medical record. Although there may be variation on the type of patient care templating system for nurses and physicians, some informational needs of nurses overlap with that of physicians. Creating an information layout that will address both the concerns of nurses and physicians means there is conformity in the patient care summary and the overall direction of the plan of care. Examples of overlapping informational needs are brief history of present admission, plan for discharge, medication regimen and contemplated surgical intervention. Doing so may boost the consistency of data shared during change of shift, facilitate the formulation of mutuality, and promote communication among disciplines [ 23].

3. Content management systems

Templating of patient care summary can be built as a module on the electronic medical record - either as custom built for the institution or shipped with the system using a predefined layout. A custom built solution is tailored to the specific needs of the organization, but may incur additional expense in planning, development, implementation, and future modifications. A patient care summary module integrated with the system would have a predefined layout, and may not allow customization based on identified needs of the hospital. This is where the CMS can be a solution to address nursing workflow.

CMSs are information technology systems that allow access, creation, modification and deletion of content based on established access roles. These systems also allow users to expand layout and functionality using plugin and theme architectures. A CMS has a more complex architecture than application frameworks that would allow non-programmers to design and build web applications without a solid background in coding. Applications built using CMSs, however, may not be that flexible, extensible or scalable than when using application frameworks (see ).

Of the various open source CMSs available, the three popular ones are WordPress, Drupal and Joomla. WordPress is the most popular CMS with the greatest install base, the highest number of searches in the Google search engine, and ranks high among CMS users when it comes to ease of use. Features and functionalities of WordPress-based applications can be extended using a variety of plugins available in the plugins directory. The presentation layout or template is also readily modifiable using themes in the theme directory [ 24]. For nurse developers with knowledge in PHP Hypertext Preprocessor (PHP) - the programming language of WordPress - they can alter the functionality and template presentation of the web applications that they are building at the code level, giving them more flexibility and creativity in integrating feature-rich modules.

WordPress started as a blogging platform that eventually expanded into a fully featured CMS and application development platform, particularly for non-programmers. Being an open source project and with the ease of use and extensibility offered by the CMS through its theme and plugin architecture, the Open Nursing Information System or Open-NIS Project was created to promote the use of CMSs , specifically WordPress and WordPress-based CMSs, such as ClassicPress, in the field of healthcare technology innovation. By providing open standards on how content management systems can be applied in the context of health information technology, the Open-NIS Project aims to promote and inspire nurses to become developers of nursing workflow systems, and be part of addressing nursing workflow issues in their respective healthcare organizations [ 25].

CMSs can be used in conjunction with electronic medical records. The CMSs can serve to augment functionalities of data handling to address nursing workflow efficiency, particularly those lacking in the electronic medical record system in terms of built-in features or customizability. Since the prominent function of an electronic health record system is to store data and make data available for retrieval and reporting, its purpose is to address the data section of the DIKW framework. CMSs could address the information, knowledge and wisdom sections of the model by storing and presenting information or data that has already been organized and synthesized by the nurse. This is analogous to how the nursing kardex was designed. The nursing kardex was not designed to replace nurse's notes, medication administration records or nursing care plans, nor is it part of the permanent record of the patient. Its main purpose is to augment nursing workflow by providing patient care summary that is relevant to how provision of care is done in a particular ward or unit [ 26]. In the same way, electronic medical records may not fully address the needs of nurses for the synthesis and transmission of information that is aligned to how processes and procedures are done in the organization or in a particular unit. This is where the value of CMSs comes in. This has been shown outside the realm of healthcare information technology, such as libraries and research institutes that also utilize WordPress and Joomla in managing data internally [ 27, 28] (see ).

4. Nurse as a developer

Nurses, as part of the healthcare force, are users or consumers of healthcare information technology innovations. These innovations have been beneficial in making workflow as efficient as possible by digitizing the storage of data and providing ease in the retrieval and manipulation of needed healthcare information. It is also interesting to view nurses as developers of health technology innovations as well, not just users of healthcare systems. Being at the forefront of patient care, nurses are valuable assets in determining how organizations can improve efficiency in the workplace.

Though a background in programming may be required to develop complex modules for electronic medical record systems, the requirement for developing applications using CMSs is not that focused on programming itself but on theme and plugin architecture - specific to a particular CMS - that is needed to modify layout and functionalities. Knowledge in coding would definitely be beneficial since the nurse developer will be able to extend the functionalities to a much greater extent than if the nurse does not have sufficient knowledge in programming. The most important aspect is to collaborate with the information technology or IT department to review the application with regards to security, and compatibility or interoperability with deployed information technology systems.

The security of an application is crucial when designing health-related programs. Whether the application built by the nurse developer was done using programming skills or with the aid of a CMS or an application-building platform, it is imperative that security code review be done, either through manual or automated means. With manual security code review, the nurse developer needs to be knowledgeable in the programming language the application was written since the review would entail browsing through program source code to detect possible vulnerabilities. This is also true with automated code review since results from automated code review would still need manual examination of code snippets to verify whether the result is a false positive or false negative based on the code functionality and security measures taken in place [ 29].

Data encryption is the process of obscuring plain text data using an encryption algorithm and key to encrypt information, and using the same key to decrypt the cipher text to present the actual data. Sensitive personal information must be encrypted. Thus, the nurse developer must programmatically devise encryption methods to protect healthcare-related data in case of a breach. In the Philippines, Section 28 ( Guidelines for Technical Security Measures) sub-section " g" of the Implementing Rules and Regulations of the Data Privacy Act of 2012 states that personal data in storage and in transit must be encrypted [ 30]. This is a concrete example of administrative and legislative measures in the Association of Southeast Asian Nations (ASEAN) region to protect personal information through data encryption.

An example of a healthcare application that nurses can delve into is undergraduate and graduate health education. A simple web application patterned from a blogging platform can serve as an electronic portfolio where faculty and professors would be able to follow through the student's progress from his blog entries. This is a basic usage of a CMS, but can have a huge impact in terms of determining student's understanding of the concepts being taught and skills acquired while in internship or residency, like health assessment, nursing care plans and relevant literature reviews [ 31, 32].

Another area of interest would be personal health records and self-management among patients with chronic conditions, such as diabetes and hypertension. Healthcare applications can be designed by nurses using CMSs that will address monitoring of patient's blood glucose levels and blood pressure, and the ability to connect with their primary health care physicians or nurse practitioners so the providers can be alerted as well with abnormal fluctuations in glucose or blood pressure levels. However, there is a need to design personal health record systems based on identified and perceived needs, and attitude of individuals. Policies should also be in place to empower individuals so that better quality data can be obtained and improve the overall health literacy [ 33].

Clinical decision support systems can be integrated in nursing healthcare applications, such as when designing a personal health record system for diabetic patients whose insulin coverage is based on a particular sliding scale set in place by the primary healthcare physician or diabetes specialist. The platform could serve as data repository for blood glucose levels and insulin coverage. The concept of clinical decision support for diabetic patients during office visits has been described [ 34]. This same paradigm can be applied in a personal health record system where the physician and patient are seen as collaborators in promoting healthcare.

The area of healthcare application development for nurses is broad and an expanding realm, and is only limited by the nurse developer's imagination. Nurses have problems and concerns in the workplace specific to efficiency in workflow. Nurses have the power to be problem solvers themselves and become nurse developers who can address these needs, using CMSs, as forefront healthcare providers.

5. Conclusion

Nurses are at the frontline of healthcare delivery service. They encounter varying and numerous workflow problems on a daily basis. They can become excellent healthcare innovators to address potential problems in their routine by adopting available and appropriate technology resources. By expanding their skillset into CMSs and information technology development as a whole, nurses have at their disposal a wide array of tools to build modern technological solutions to address difficulties that could affect patient care or nursing workflow. However, with the availability of technological tools, nurse developers should be knowledgeable and responsible on how to properly integrate or implement these advancements into their current information technology systems, in coordination with the information technology department. This collaboration would make sure the health information technology solution uses appropriate technologies to capture needed healthcare information.

Nurse developers can also work independently to build application platforms or software-as-a-service infrastructures to improve the quality of patient care and nursing service. Being an independent creator of health information technology solutions, the use of CMSs would allow rapid application development to achieve a minimum viable product needed to test whether the innovative idea is sound and feasible. CMSss and low or no-code application development platforms are widely available for nurse developers who are interested in developing database-driven applications. However, the nurse developer must still be able to examine the application at the programming code level, particularly during security code review, to show credibility and elicit confidence from users. Nurse developers should be mindful when handling patient or user information by adhering to information technology best practices, such as security code review and data encryption of sensitive personal information - whether in storage or in transit, to protect healthcare data.

Conflicts of interest

None.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

References

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Use of content management systems to address nursing workflow

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Nurses are at the forefront of providing healthcare services to individuals of all age groups and with varying medical conditions. Aside from the critical knowledge and technical skills from nursing science, advancement in technology has assisted nurses in delivering quality nursing care by streamlining workflow processes and ensuring that data can easily be retrieved or modified.

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Week 4 transforming clinical date into wisdom

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Week 4 transforming clinical date into wisdom

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018525/

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Kenrick D. Cato, PhD, RN, CPHIMS, FAAN, assistant professor, Kathleen McGrow, DNP, MS, RN, PMP, chief nursing information officer, and Sarah Collins Rossetti, RN, PhD, FACMI, FAMIA, assistant professor

Artificial intelligence (AI) is part computer science and part cognitive science, encompassing the phenomena of computers performing tasks that require human intelligence.1 Current interest in AI is motivated, in part, by recent developments in machine learning, in which algorithms learn from data without human direction.2 Machine learning is a group of different mathematical methods to build AI rules. Each of these methods has its strengths and weaknesses based on the types of data being analyzed. Some machine learning approaches, such as speech recognition when talking into the speaker of a smart phone or smart home device, are now mainstream.

Although we've been on a journey for decades to augment human capabilities, recently there's been a dramatic rise in AI research in healthcare. AI has been brought to the forefront due to multiple factors, including the tremendous amounts of data that are captured and stored through electronic health records (EHRs), more availability of computer power, and significant innovations in AI tools and algorithms. The increase in AI availability and use in healthcare makes it imperative that both nurse leaders and nurses have a fundamental understanding of AI when used in conjunction with clinical models, critical-thinking skills, and evidence-based practice.

Graves and Corcoran identified the conceptual model that's pivotal to nursing informatics known as the data, information, knowledge, wisdom (DIKW) framework.3 This framework has subsequently been used to define how nursing informatics impacts nursing practice and care delivery, and it aligns well with the American Nurses Association Nursing Informatics: Scope and Standards of Practice, which identifies nursing informaticists as assisting in the development of wisdom for all nurses.4 In addition to the use of knowledge, wisdom includes decision-making and applying nursing actions through the applicable use of data, information, and knowledge.5,6 In the healthcare setting, AI is often paired with a clinical decision support (CDS) application to move from knowledge to wisdom, which involves exercising judgment.7 The ability to use AI-assisted wisdom to solve problems and identify solutions is key for nurses to enable the delivery of nursing care that's more precise and efficient, benefiting both nurses and patients.8

In this article, we explain the DIKW framework, review key concepts of AI and CDS, offer examples of real-world AI/CDS applications within the DIKW framework and how they can be used, and identify relevant questions to ask when investigating AI for use in clinical and operational scenarios.

DIKW, AI, and CDS

The DIKW framework explains that data leads to information, information to knowledge, and knowledge to wisdom.9 This framework is particularly relevant when designing CDS tools to advance nursing practice to top of licensure for improved nurse decision-making. The DIKW framework can be used to categorize CDS tools, including those that are driven by AI. This framework is pyramidal, which implies that data are profuse and wisdom is rare. As defined in previous publications discussing this framework, data have little or no meaning in isolation, information is data plus meaning, knowledge is derived by discovering patterns and relationships between types of information, and wisdom is understanding and internalization of knowledge patterns and relationships. (See Table 1 .)10 The DIKW framework isn't just a way to understand how information can be processed; it's also a means by which nurse leaders can strategically evaluate the relationship between AI and CDS.

Table 1.

Concept

Definition

Wisdom

Understanding and internalization

Knowledge

Derived by discovering patterns and relationships between types of information

Information

Data plus meaning

Data

Little or no meaning in isolation

AI generally refers to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention...[these machines] make decisions which normally require human level of expertise."11 West and Allen describe three characteristics of these systems, which "operate in an intentional, intelligent, and adaptive manner."12 Intentionality is defined as algorithms designed to make decisions, typically using real-time data.12 Intelligent systems use underlying trends or patterns to inform decision-making.12 And an adaptive manner includes the ability to learn and improve decisions based on new information or pattern recognition.12 In the healthcare setting, AI is used with analytics to support CDS, improve operational efficiency, and for general cost effectiveness.13

CDS and AI aren't one in the same. CDS provides clinicians with computer-generated clinical knowledge and patient-related information that's intelligently filtered and presented at appropriate times to enhance patient care.14 In other words, AI can be thought of as a fuel to power more intelligent CDS. For example, imagine having a CDS alert for nurses to help them manage a patient with diabetes based on information from tens of thousands of patients and other nurses in a similar situation. Even though CDS interventions can be used to implement AI at the point of care, they don't always leverage AI. In fact, a 2017 integrative review identified that only 25% of CDS targeting nurses uses real-time EHR data.15

An analysis of requests for development of CDS targeted at nurses identified that the majority of requests were information and knowledge related when plotted along the DIKW framework.16 There's untapped potential for use of AI within CDS interventions that target risk assessment/risk reduction activities within the nursing process.16 These areas include AI-based CDS interventions that align with the wisdom concept from the DIKW framework and use, for example, predictive analytics, personalization of information presentation, patient preferences, and social and behavioral determinants of health to guide nurses' understanding and internalization of knowledge patterns and relationships.16

Targeted CDS nursing interventions shouldn't stop at the planning stage of the nursing process as many do currently.16 To impart wisdom--understanding and internalization of knowledge patterns and relationships--AI designed for nurses should be intentionally designed to use real-time data to recommend decisions, have intelligent recognition of patterns within nursing and patient care workflows and processes, and be adaptive by aligning with the cyclical nature of workflow activities in the nursing process.

In healthcare, there's been an increase in AI/CDS-based tools. Some of these offerings don't solve critical nursing decision support needs. However, there are AI/CDS tools that nurse leaders can benefit from; for example, tools that provide predictive analytics for staffing and bed management, to name a few. The DIKW model can help leaders understand which of these AI/CDS tools will be useful in their organization. Figure 1 illustrates the hierarchical nature of AI/CDS applications in which there are data capture, data processing to meet information needs, clinical knowledge-based rules to guide decision-making, and exposed analytics in the form of wisdom to augment nurse decision-making.16

From a clinical user perspective, the bottom three levels of the application usually aren't visible. The user often sees the top, fourth level in the form of an alert, dashboard, risk score, app, or other interface. However, when investigating AI/CDS applications, it's essential to understand how all of the levels were developed and validated.

Real-world examples

There are numerous examples of AI/CDS applications for nurses, such as the AI-based sepsis CDS alerts used in many EHRs today.17-22 There are also newer AI/CDS tools, such as the Communicating Narrative Concerns Entered by RNs (CONCERN) CDS system. CONCERN is an AI/CDS tool that predicts a patient's risk of clinical deterioration, which is currently being used at Partners Health System, with a planned rolled out at NewYork-Presbyterian Hospital later this year.23 Both the sepsis CDS tool and the CONCERN CDS system illustrate how the DIKW approach can be used by nurse leaders to determine if these technologies make sense in their clinical setting.

A number of different types of AI rules have been built to trigger sepsis CDS alerts.24 The data that most of these sepsis alerts use are vital signs and lab results. Machine learning is then used to build AI-based rules to predict patients who are at risk for sepsis. The information generated is the meaning of different types of lab results and vital signs, and the knowledge is the predicted increased risk. The current sepsis CDS alerts don't reach the wisdom level for two reasons. First, because the sepsis alerts are delivered by the EHR, there's a false alarm or fatigue alert issue. This situation happens because no AI is perfect; many patients receive the sepsis alert, disrupting the nurse's regular workflow when the patient clearly isn't at risk for sepsis. Eventually, nurses get tired or fatigued by these alert-based disruptions and ignore the CDS altogether. Second, most sepsis alerts trigger 4 hours before it's clinically clear that the patient is significantly deteriorating.24 This doesn't allow clinicians much time to intervene and effectively treat the patient.

The CONCERN CDS identifies nursing documentation patterns that are a proxy of a nurse's concern for patient deterioration and generates a predictive early warning score. The CONCERN CDS consists of two main components: the decision engine, which uses AI models to analyze the content and patterns of nursing EHR data (such as flowsheet entries and nursing notes), and front-end interfaces that display the predicted CONCERN levels (green, yellow, red), with red indicating that a patient is at the highest risk for deterioration. A web application provides tracking of the CONCERN level over time and facilitates transparency of the AI models by displaying what factors contributed most to the current calculation.

The result of more than 8 years of qualitative and quantitative research involving clinician input at every stage, the CONCERN CDS is built with the DIKW framework in mind. Data capture is automatic from the EHR, with the AI model using data entered during the normal nursing workflow. The nursing data used include patterns and content of nursing notes, medications administration record information, and flowsheet entries. The system continuously monitors data inputs from patterns in nursing documentation and flowsheets to model performance. Alerting tools let the programmers who maintain the system know when expected data feeds are missing or the model output is different than expected.

Information generation is based on focus group and simulation testing with nurses and physicians. An example of knowledge representation is the green, yellow, red scoring system that indicates the patient's risk of deterioration. When the CONCERN CDS was being developed, nurses and physicians indicated in focus group and simulation testing that colored levels for risk of deterioration would be the most clinically meaningful. Also, the setting-specific (acute care versus ICU) CONCERN AI models are optimized to identify patients who score in the yellow category. Clinicians reported that patients at the lowest and highest risk for deterioration are much easier to identify. Patients at the yellow level are at increased risk for clinical deterioration but haven't started showing signs yet. Figure 2 shows a sample patient list in which users can quickly see a patient's CONCERN level in their workflow.

The CONCERN CDS is designed to augment clinical expertise instead of replacing it. Figure 3 shows the main CONCERN web application, which opens when a clinician clicks on a CONCERN level in the patient list. The main decision support part of the CONCERN CDS system is focused on visualizing trends in patient risk. The factors portion of the CONCERN CDS provides AI transparency, explaining what elements of the patient record contributed to the risk calculation. Additionally, a major goal of CONCERN CDS system is to support interdisciplinary communication by providing the relevant information (notes, medications administered, flowsheet entries) for patients at increased risk for deterioration in a centralized, accessible location many hours before the patient crashes. The CONCERN score has been shown to trigger up to 24 hours before a clear deterioration event.

Questions for nurse leaders to ask

With the abundance of data inside and outside of the EHR, AI/CDS applications are being used more frequently by nurses to provide care for patients.25 However, research indicates that the success of new AI/CDS systems requires careful planning, evaluation, and decision-making.15,26-28 AI/CDS applications are often presented as solutions to pressing clinical problems. The DIKW framework can help nurse leaders organize strategic planning and prioritization of potential AI/CDS applications, helping connect clinical practice with informatics solutions and revealing potential nursing practice or informatics gaps for proposed CDS.16 Table 2 describes a number of questions based on the DIKW levels that nurse leaders should ask about AI/CDS systems.

Table 2.

DIKW level

Question

Explanation

Data
What data is used in the AI/CDS tool?

It is important to understand the required datasources. Also, what types of transformations that are done on the data that might change it's clinical meaning. For example, what are the cutoffs for how results are categorized?

How is it captured? Does this data capture fit into the existing clinical workflow?

It is important to know if data capture will add any additional burden to clinicians. Also, will clinicians workflow be impacted by required data capture?

Is there an appropriate life cycle plan for the CDS?

Data capture is dynamic, with continuous changes in the configuration. For example, forms in the EHR will be updated, created, or retired. Is there a similar life cycle plan for the CDS.18 How will the CDS handle EHR changes? Is there de-implementation planning?

Information
Does the AI/CDS information take into account the clinical context?

The CDS should be agile enough to adapt to changes in clinical settings. For example, the same models should not be applied across settings where workflow and policies are different, for example acute care versus intensive care settings.

Does the information produced make clinical sense and have clinical relevance?

You should ensure that clinicians review the decision support and validate the clinical appropriateness and relevance of the CDS recommendations.

Knowledge
Does the AI/CDS help to solve a clinical problem. What were the examples that were used to teach the model?

For, AI/CDS, the products of the model are only as good as the data and information that helped create it. 22

Does the clinical decision support fit nursing processes?

Effective CDS should adhere to nursing science and processes.

Wisdom
Is the AI/CDS augmenting or taking over decision making?

The goal of effective AI/CDS should be to aid in decision making not replace the clinician particiapation.23

Is the AI explainable to the clinician?

AI/CDS will not be effective if clinicians don't understand how the recommendation are produced.24

Is the required short-term and long-term training in place?

Continuous and effective training is required to be confident that AI/CDS users know what recommendations represent and how to use them in their clinical practice?25-27

Strategic implementation

AI-powered CDS tools offer great promise in helping nurses to provide higher quality, safer, and more efficient care. It's imperative that nurse leaders understand the key concepts of AI and CDS. The DIKW framework and relevant questions support nurse leaders in thinking strategically about current and future implementation of AI/CDS applications.

​

There are a number of exciting examples of these new AI/CDS applications, among them is the Communicating Narrative Concerns Entered by RNs clinical decision support CONCERN CDS system. The CONCERN CDS is an AI/CDS that uses technology that has identified nursing documentation patterns that are a proxy of a nurses concern for deterioration and includes these proxies as features in a predictive early warning score. The CONCERN CDS is consists of two main components. First, the decision engine that uses AI models to analyze the content and patterns of nursing EHR data (e.g. flowsheet entries and nursing notes ). Second, front-end interfaces display the predicted CONCERN levels (green, yellow, red), with red indicating a patient is at highest risk for deterioration. A web application provides tracking of the CONERN level over time and facilitates transparency of the AI models by displaying which factors contributed most to the current calculation.

Acknowledgments

This study was funded by NINR 1R01NR016941-01, Communicating Narrative Concerns Entered by RNs (CONCERN): Clinical Decision Support Communication for Risky Patient States. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The authors have disclosed no financial relationships related to this article.

Contributor Information

Kenrick D. Cato, Columbia University School of Nursing in New York, NY.

Kathleen McGrow, Microsoft Corporation in Redmond, Wash.

Sarah Collins Rossetti, biomedical informatics and nursing at Columbia University in New York, NY.

REFERENCES

4. American Nurses Association. Nursing Informatics: Scope and Standards of Practice. 2nd ed. Silver Spring, MD: American Nurses Association; 2015. [ Google Scholar]

23. Rossetti SC, Knaplund C, Albers D, et al. Leveraging clinical expertise as a feature--not an outcome--of predictive models: evaluation of an early warning system use case. AMIA Annu Symp Proc. 2020;2019:323-332. [[ PubMed] PMC free article] [ Google Scholar]

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Kenrick D. Cato, PhD, RN, CPHIMS, FAAN, assistant professor, Kathleen McGrow, DNP, MS, RN, PMP, chief nursing information officer, and Sarah Collins Rossetti, RN, PhD, FACMI, FAMIA, assistant professor Artificial intelligence (AI) is part computer science and part cognitive science, encompassing the phenomena of computers performing tasks that require human intelligence.1 Current interest in AI is motivated, in part, by recent developments in machine learning, in which algorithms learn from data without human direction.2 Machine learning is a group of different mathematical methods to build AI rules.

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https://www.nursingcenter.com/journalarticle?Article_ID=3303329&Journal_ID=54009&Issue_ID=3302962

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The data-information-knowledge-wisdom (DIKW) model has been widely adopted in nursing informatics. In this article, we examine the evolution of DIKW in nursing informatics while...

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https://nursing-informatics.com/blog/theory-applied-to-informatics-dikw-theory/

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by June Kaminski, RN MSN PhD(c)
Editor in Chief

CJNI was initiated by June Kaminski in 2006 when she was President-Elect of CNIA. She is currently Faculty and Curriculum Coordinator of a BSN Advanced Entry nursing program at Kwantlen Polytechnic University; Communications Officer, Webmaster, and former President of the Canadian Association of Nurses for the Environment and Editor in Chief of the Online Journal of Nursing Informatics. In 2012, June was honoured to receive the CASN and Canada Health Infoway's inaugural Nursing Faculty E-Health Award 2012 in Ottawa Canada. She also won the Distinguished Teaching Award from Kwantlen Polytechnic University in 2016. She offers the Nursing Informatics Learning Center with accredited CEU informatics courses.

Citation: Kaminski, J. (2021). Theory applied to informatics: DIKW Theory Editorial. Canadian Journal of Nursing Informatics, 16(3-4). https://cjni.net/journal/?p=9374

EDITORIAL

Theory is an important aspect of nursing informatics - one that is often neglected due to time and context. The DIKW theory, short for Data - Information - Knowledge - Wisdom has evolved over the years across a variety of disciplines and is one of the most embraced in nursing informatics and "considered by some as the canon of information science and knowledge management" (Gackowski, 2012, p. 93) and one of the most widely recognized theories in informatics (McDowell, 2021).

Charlene Ronquillo, Leanne Currie and Paddy Rodney (2016) described the DIKW as being both a middle-range theory and a grand theory. "DIKW falls under the criteria of a grand theory, as it outlines a theoretical framework of abstract concepts with specific relationships that attempt to explain a specific phenomenon (the movement from data to wisdom), with limited capacity for empirical testing. However, DIKW also has features of middle-range theory such as having implicit philosophical assumptions, being sufficiently general to cross multiple clinical populations, and providing specifics to guide research and practice. DIKW explicitly outlines each step of the theoretical framework and provides clear direction as to how data can be processed and transformed from discrete entities to components of a complex knowledge base and further, describing how arriving at wisdom ultimately guides clinical practice and decision making" (p. E.15).

Figure 1: DIKW Theory Origins: T.S. Eliot (1934)

Evolution of the Theory

Several authors have outlined the evolution of the DIKW theory, some with alternate branches and acknowledgements. Most credit the first mention of this paradigm to none other than well-known author, T. S. Eliot who referred to at least part of it in his play, The Rock in 1934. The particular stanza that is credited goes like this (Figure 1):

In 2008, Nikhil Sharma credited the next step in defining this theory to Harland Cleveland (1982) who wrote about information, knowledge and wisdom in his Futurist article. Michael Zeleny (1987) is credited with the first formal treatise on the dimensions as he strove to describe how to achieve integrated knowledge management "by equating Data, Information, Knowledge and Wisdom to various knowledge forms: "know nothing", "know what", "know how" and "know why" respectively" (Sharma, 2008, p. 2). Zeleny (1987) also proposed a fifth tier, enlightenment, which became the apex of the pyramid above wisdom. Russell Ackoff (1989) is often credited as the originator of the DIKW when he presented a 1988 Presidential Address to the International Society for General Systems Research (ISGSR). Ackoff described the four dimensions with an added fifth dimension of "understanding" as a linking concept between knowledge and wisdom, "thus Ackoff's hierarchy is Data, Information, Knowledge, Understanding, and Wisdom" (Sharma, 2008, p. 2).

Figure 2: Classic DIKW Pyramid

Bellinger et al., (2004) theorized that Understanding was not a separate tier of the hierarchy, but rather, a connecting concept that wove the other four together. Thus, data led to information by understanding relations; information led to knowledge by understanding patterns and knowledge could lead to wisdom by understanding principles. "Understanding is therefore identified as the transformational relationship among data, information, knowledge, and wisdom to create an outcome at a higher level" (Faucher et al., 2008, p. 7) through the process of connectedness. "Many informed people know what to do, quite a few knowledgeable experts know how to do it, but only a few wise persons know and can fully explicate why it should be done" (Aven, 2013, p. 31).

This theory is often presented in the form of a pyramid or hierarchy (Figure 2) with data at the bottom and wisdom at the top, however, most point out that the steps are not linear but interwoven (Frické, 2009, 2019). The pyramid shape suggests the simple to complex properties of the theory (Decker et al., 2018, p. 300). "Information consists of data, but data is not necessarily information. Also, wisdom is knowledge, which in turn is information, which in turn is data, but, for example, knowledge is not necessarily wisdom. So, wisdom is a subset of knowledge, which is a subset of information, which is a subset of data. The terms Data, Information, Knowledge, and Wisdom are sometimes presented in a form that suggests a scale. However, in no sense do these four terms define some sort of linear equal-interval scale" (Alivelu et al, 2015, p. 3). On the other hand, Neumann (2020) mused that "each step up the pyramid answers questions about and adds value to the initial data" reinforcing the hierarchal structure of this theory (p.1).

Bierly, et al. (2000) described the DIKW theory in the context of educational theory, linking each of the four dimensions to other educational theories, including Benjamin Bloom's (1956) Taxonomy of Educational Objectives. Data was described as raw facts that are accumulated in the learner through memorization and accumulation of facts (first level of learning). Information is data that is processed by a learner to become meaningful and useful, thus gives form and functionality to data through comprehension (second level of learning). Knowledge is distinguished as "knowing how" or tacit knowledge and "knowing why" or explicit knowledge coupled with the cognitive processes of analysis and synthesis (Bloom's fourth and fifth level of learning) which includes "an understanding of information and associated patterns" (Bierly et al., 2000, p. 601). Finally, wisdom is equated with Bloom's sixth level of Evaluation, defined as "the ability to best use knowledge for establishing and achieving desired goals and learning about wisdom as the process of discerning judgements and action based on knowledge" (Bierly et al., 2000, p. 601).

Jonathan Hey (2004) took a metaphorical approach to analyze the DIKW theory, affording an interesting view on how we talk about the firs three tiers - data, information, and knowledge. Particularly, in this day and age of copious amounts of data and information, these metaphors make us think. For instance, data is often alluded to as "data streams" or we are "drowning in data." People often refer to the "volume of information" available, or that information "pours across the internet ", "information flows across channels" or we are "bombarded with information." Knowledge is often viewed as tacit or "personal and context-specific" or explicit. "It is explicit knowledge that most current knowledge management practices try to, and indeed are able to, capture, acquire, create, leverage, retain, codify, store, transfer and share" (p. 10). Metaphorically, knowledge can be "sticky" or "leaky" or "crystallized". Hey concluded that "this analysis seems to add weight to the visual metaphor provided by the 'Knowledge pyramid' where large amounts of data are distilled to a smaller quantity of information, which is, in turn, aggregated to create yet more distilled, though more widely applicable, knowledge" (2004, p. 15).

Colonal Harry Tunnell (2014) examined the fit between the DIKW theory and network-centric war commanding and surmised that the "DIKW hierarchy is a knowledge management structure that helps people make data become meaningful for decision making" (p. 45). Despite its common use, the DIKW theory has had both good and bad reviews (Ronquillo et al., 2016; Frické, 2009, 2019). Sasa Baškarada and Andy Koronios (2013) defended the utility of the DIKW despite many critics: "Some have gone even further, arguing that the hierarchy is based on flawed assumptions and that it should be abandoned altogether (Frické, 2009). However, given that most information systems literature relies on these concepts, abandoning the hierarchy does not seem feasible. As a result, developing clear, consistent, and unambiguous definitions of the terms, their relationships, and their quality dimensions is imperative" (p. 8).

To understand the theory, the four dimensions: data, information, knowledge, and wisdom should be examined.

Data

Data are considered the simplest and most basic element of the theory, and come in many forms: numbers, letters, images, sentences, and so on. Electronic health records are rich in client data. "Indeed, the appeal of DIKW relates to its fit with the way computers process information. In the context of computerized systems, it is logical and necessary to view data as value-free facts that are observable and measurable in order for computerized information systems to function. Furthermore, the propositions that data are basic, discrete, "building blocks," processed data and addition of context creates information, and synthesizing information and identifying patterns and relationships create knowledge are features that can be programmed into computerized clinical information systems" (Ronquillo, et al., 2016, p. E.11).

With the advent of big data, the whole concept of data has skyrocketed far beyond was conceived as data back in the 1980s when the theory was first emerging. "Big data also captures what we have conventionally not regarded as data. To the conventional varieties - facts, signals, symbols - big data captures behaviour, emotions, actions, and attitudes as raw data. Consumer behaviour is captured as data routinely by online shops. Mood and emotions in images, videos, audio and other digital media can be recognised and captured as data. Whereas laborious surveys gathered data on attitudes, now it is instantly acquired simply by clicks. All of our interaction on such platforms as Facebook, Instagram, Twitter, our language and expressions, our likes and shares, are turned into data. Indeed, almost every aspect of what makes an individual truly individual, a community, the fundamental properties that define their identities are quantified and seized by big data. In short, big data incorporates the essence of individuals, groups and communities" (Sardar, 2020, p. 5).

Information

In this theory, information emerges from data. "Information may be thought of as "data plus meaning." It can be derived by processing data. Information represents the facts and ideas that are available to be known within a certain context. When data are put into a context and combinedwithin a structure, information emerges" (Matney et al., 2011, p. 8). Information has become so common and so profuse that our current era has been dubbed "the information age" where "we over-invest in information and under-invest in knowledge and wisdom" (Mayer, 2005, p. 69).

Tunnel described information as "data that have been transformed to have meaning for human beings by being organized with specific relationships between the data. Information adds value to a person's understanding about something. People use an IS to perform this transformation" (2014, p. 45).

Knowledge

The third dimension of the DIKW is knowledge, a concept that has become both elusive yet used continuously. "Knowledge has six key characteristics: it is subjective as well as objective, it is context sensitive, it has both collective and personal components, it has a tacit and explicit nature, it has a limited shelf life in terms of usability and infinite shelf life as a piece of information, and it is functional when you apply it and informational when you acquire it" (Hoppe, 2011, p. 155). Knowledge is a process of making meaning of information, "however, without the dimensions of context, culture, tacitness, and time, this knowledge would be little more than information" (p. 155).

"Explicit knowledge is what knowledge management systems (such as computer decision support systems) seek to capture, codify, store, transfer, and share. Like the transformation of data to information, the transformation of information to knowledge in nursing and information systems can be conceptualized as a continuum of increasing understanding. Knowledge is derived by discovering patterns and relationships between types of information" (Matney et al., 2011, p. 8).

"Big data and gargantuan information have radically transformed modes of the production of knowledge as well as the nature of knowledge itself. We describe big data and gargantuan information driven knowledge as emergent because it is a product of interconnected, networked, evolving components: that is to say it is a complex system, that can spontaneously generate order, adaptation, feedback loops. Emergent knowledge has no borders: it is intrinsically multi-, inter- and transdisciplinary; it is simultaneously global and local; it codifies both the external and internal features of its subjects and objects. It incorporates and commodifies both the essence and being of individuals, groups and communities. It is contradictory and chaotic. And it can produce totally new manifestations of itself - which cannot be predicted, or indeed bear no relation to, its component forms" (Sardar, 2020, p. 7).

"In summary, there is general agreement that there is a relationship between knowledge and wisdom, but that relationship is complex. Being knowledgeable is not the same thing as being wise. A knowledgeable person has a complex explicit and tacit knowledge base on which to draw and has the capacity to understand complex situations. A wise person has, in addition the capacity to demonstrate sound judgement, which takes into account social, ethical, relationship organisational and environmental issues. When they exercise wisdom, they use this capacity to promote decision making and action" (Rowley, 2006b, p. 258).

Wisdom

Wisdom is perhaps the most elusive dimension in this theory and has been defined in a myriad of ways throughout human history (Kaufman, 2006; Kupetz, 2013; Miscevic, 2012; Murray, 2010; Ryan, 2012). In the context of this theory, the following definition seems to be one of the most poignant and applicable: "Wisdom or sapience is the ability to think and act using knowledge, experience, understanding, common sense, and insight. Wisdom has been regarded as one of four cardinal virtues; and as a virtue, it is a habit or disposition to perform the action with the highest degree of adequacy under any given circumstance, and to avoid doing anything wrong. This implies a possession of knowledge or the seeking of knowledge application to the given circumstance. This involves an understanding of people, objects, events, situations, and the willingness as well as the ability to apply perception, judgement, and action in keeping with the understanding of what is the optimal course of action. It often requires control of one's emotional reactions (the "passions") so that the universal principle of reason prevails to determine one's action" (Bohle, 2016, p. 140).

Targowski (2005) mused that wisdom is "a pragmatic unit of cognition that generates volition - a chosen way of acting and communicating. Wisdom is a process of choosing among available concept options, based on knowledge, practice, morale, or intuition, or on all of them" (p. 61).

Wisdom can also be described as "the application of intuition to accumulated knowledge applied in a visionary or anticipatory manner. People do not use an IS to create wisdom. Rather, they review knowledge discerned through data-information-knowledge transformations and apply personal intuition to create wisdom" (Tunnel, 2014, p. 45).

According to Jennifer Rowley, "The wisdom hierarchy suggests that:

Figure 3: DIKW Theory Pyramid (in mine, Wisdon enfolds the other three dimensions)

There is more data than information, than knowledge, than wisdom. Is this desirable and acceptable, or simply a statement of the status quo?

The hierarchy with its broad base of data is safe, secure and stable.

Wisdom is only attained after much processing of data, information and knowledge, and the process starts with data" (2007, p. 175).

"We therefore define wisdom as: the capacity to put into action the most appropriate behaviour, taking into account what is known (knowledge) and what does the most good (ethical and social considerations). This leads to a simple relationship between knowledge and wisdom. If knowledge is seen as "knowing how", wisdom is "knowing why, what and how" to do something" (Rowley, 2006a. p. 1250). Rowley and Slack (2009) pointed out that a common theme in the literature on wisdom is its inherent link with action and judgement, and the knowledge of what is good and right (ethics) which inform strategic decision-making and making choices. Perhaps wisdom should enfold the other parts of the pyramid and knowledge should be at the central base (Figure 3).

Liew (2013, p. 59) and Efe (2016, p. 8) described six wisdom dimensions to consider:

1. Reasoning ability-The unique ability to look at a problem or situation and solve it, good problem solving abilities, a logical mind.

2. Sagacity-Considers advices, understands people through dealing with a variety of people, feels he/she can always learn from other people, is fair.

3. Learning from ideas and environments-Attaches importance to ideas, looks at different perspectives, learns from other people's mistakes.

4. Judgment-Acts within own physical and intellectual limitations, sensible, has good judgment at all times, thinks before acting or making decisions.

5. Expeditious use of information-Is experienced; seeks out information, especially details; learns and remembers and gains information from past mistakes or successes.

6. Perspicacity-Offers solutions that are on the side of right and truth, able to see through things reading between the lines, the ability to understand and interpret his or her environment.

"Wisdom can thus be seen as a highly integrative, emotional and conscious, creative process" (Hoppe et al, 2011, p. 587). "The path to wisdom is then knowing when to differentiate, when to integrate, when to go after what one intends, and when to let go and `flow' with the challenges presented" (Kaipa, 2000, p. 157). Some would say wisdom is developed over time in many nurses and is applied routinely during nursing practice. "In nursing, wisdom guides the nurse in recognizing the situation at hand based on patients' values, nurse's experience, and healthcare knowledge. Combining all these components, the nurse decides on a nursing intervention or action" (Topaz, 2013, p. 2).

Pauleen et al., (2017) described Social Practice Wisdom (SPW) defined as "the art of living coherently and appropriately according to the situation to produce excellence for oneself and others. Wise practitioners do this by integrating intellectual, emotional and ethical virtues in practice in a specific (cultural, social, political and economic) context, time and place. In this view, wisdom is the peak of human social excellence" (p. 403).

A collective approach to wisdom could also be transposed to healthcare and the work of nurses within it. "We need to move from the conventional notion of wisdom as a repository of individual quality, the prerogative of sagely individuals to a more profound understanding: wisdom as a collective, communal, enterprise. In postnormal times, wisdom has to be seen as a collective moral acumen; a rational cooperative learning how to live sustainably; a communal effort to create what is truly of significance; what enhances quality of life, human well-being, and augments the health of the planet; and what plants the seeds for a genuine future of justice and equality" (Sardar, 2020, p. 10).

DIKW Theory and Nursing Informatics

Although DIKW theory evolved outside of the healthcare realm, further evolution and application within the sphere of nursing informatics has been robust (Nation & Wangia-Anderson, 2019; Matney et al., 2011; Nelson, 2018, 2020; Topaz, 2013; Ronquillo et al., 2016; Gee et al., 2012). "Data, information, knowledge, and wisdom (DIKW) provide a foundational framework for NI and the framework is useful for the broader nursing community as well, providing a basis for linking theory and practice" (Matney et al., 2011, p. 6). The American Nurses Association (ANA) included the DIKW theory in their 2008 definition of Nursing Informatics: "Nursing informatics (NI) is the specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology" (p.1).

In 2015, the ANA further expanded their definition to "Nursing informatics is the specialty that integrates nursing science with multiple information and analytical sciences (a listing of sciences that integrate with NI includes, but is not limited to: computer science, cognitive science, the science of terminologies and taxonomies [including naming and coding conventions], information management, library science, heuristics, archival science, and mathematics") to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology" (ANA, 2015, p. 1).

This new definition reinforced the importance of the DIKW by "expanding the actions from management and communication of data, information, knowledge, and wisdom to include the precursors, identification, and definition. This change reflects the important role of today's informatics nurse in the full evolutionary transformation of data to information to knowledge to wisdom in nursing practice" (Bickford, 2015, p. 129).

Judith Graves and Sheila Corcoran (1989) are credited as being the first nurses who tied together the first three DIKW dimensions, data-information-knowledge in the context of nursing informatics. "This framework for nursing informatics relies on a taxonomy and definition of the central concepts of data, information and knowledge put forward by Blum (1986), who defines data as discrete entities that are described objectively without interpretation, information as data that are interpreted, organized or structured and knowledge as information that has been synthesized so that interrelationships are identified and formalized" (p. 228).

Data, information, and knowledge were presented as intrinsically linked, working together to inform and support nurses in practice. "In practice, processing is considered as a transformation of data or information from one form to another form, usually at a more complex state of organization or meaning. There is a progression of transformation of data into information and of information into knowledge. Knowledge is used both in making decisions and in making new discoveries" (Graves & Corcoran, 1989, p. 228).

Bruce Bulm (1986a) is credited by Graves and Corcoran (1989) as being the first to apply the data-information-knowledge dimensions to medical informatics. Bulm (1986b) described these dimensions in the context of clinical information systems in the following way:

Data. These are the uninterpreted items given to an analyst or problem solver, such as the signals processed by an electrocardiograph or an imaging device.

Information. This is a collection of data elements organized (or interpreted) to convey meaning to the user, such as an automated medical record or a flow sheet.

Knowledge. This is the formalization of the relationships, experience, rules and so forth by which information is formed from data, such as signal-processing algorithms and the knowledge bases of expert systems (p. 791).

Ramona Nelson (2018, 2020) described how she added wisdom to the data-information-knowledge triad in 2008 with coauthor Irene Joos. "In 1989, a colleague and I wrote a brief article defining the concepts of data, information, knowledge, and wisdom. (Nelson & Joos, 1989 Fall). At that time, the three concepts of data, information, and knowledge were well established in the field of information science and had been introduced in the emerging discipline of medical informatics. However, adding the concept of wisdom to these three concepts and defining how wisdom was related to the established concepts was new" (Nelson, 2018, p. 1). According to Nelson (2018) "the wisdom of nursing is demonstrated when the nursing data, information and knowledge are managed and used in making appropriate decisions that meet the health needs of individuals, families, groups and communities" (p. 2).

In her 2020 article, Nelson presented renditions of a visual representation of the DIKW model comprised of four interlinking circles, one for each dimension. Within this linked chain, action words were attributed to each dimension to further clarify how they were interconnected. Data was enhanced with the actions of "Naming, collecting, and organizing" leading to Information with actions of "Organizing and interpreting", which led to Knowledge with actions of "Interpreting, integrating, and understanding" and culminating in Wisdom with actions of "Understanding, applying, and integrating service with compassion" (p. 2). The visual also emphasized the importance of increasing complexity as one moves through the four dimensions, the presence of constant flux, and increasing interactions and inter-relationships between the four DIKW dimensions.

In health informatics in general, the advances of big data accumulation provides an opportunity for health professionals to find ways to apply this data into usable information and subsequent evidence-based knowledge and perhaps even wisdom through the use of human and artificial intelligence (AI) that can be applied in meaningful ways to improve practice and outcomes (Car et al., 2019). Cato (2020) also looked at the DIKW theory in the context of harnessing AI in nursing, explaining that nurse leaders need to understand that "data have little or no meaning in isolation, information is data plus meaning, knowledge is derived by discovering patterns and relationships between types of information, and wisdom is understanding and internalization of knowledge patterns and relationships" (p. 25). Cato and colleagues elaborated that a variety of advanced tools can be viewed to catalog "the hierarchical nature of AI/CDS applications in which there are data capture, data processing to meet information needs, clinical knowledge based rules to guide decision making, and exposed analytics in the form of wisdom to augment nurse decision-making" (p. 26).

Dammann (2018) proposed an alternative theory to DIKW using evidence instead of wisdom and putting knowledge at the peak of the pyramid resulting in a Data, Information, Evidence, Knowledge hierarchy (DIEK). Dammann rationalized this approach by explaining "I do not think that wisdom adds much to the decision-making based on the hierarchy. Instead, I hold that knowledge deserves the position at the pinnacle of the hierarchy. Knowledge can be defined, in the context of medical and public health informatics and data science, as predictive, testable, consistently successful belief, if there is a causal connection between the facts represented by the data, information, and evidence on the one hand, and our beliefs on the other" (p. e.226). Raw data is contextualized into usable information, which is then compared to standards, values, and so on to become evidence, which can rise to become evidence-based knowledge.

Aven (2013) theorized how the DIKW theory can inform risk assessment/analysis.

Data-The input for the risk assessment

Information-The risk description and definition.

Knowledge (for the decision maker)-Understanding the risk description and its impact on organizational goals and objectives.

Knowledge (for analysts)-Understanding how to do the risk assessment and understanding the risk description in order to set the best control activities to manage risk.

Wisdom (for the decision maker)-The ability to use the results of the analysis in the right way.

Wisdom (for analysts)-The ability to present the results of the analysis in the right way (p. 33; Efe, 2016).

Ahmed Efe (2016) expanded the perspective of the DIKW theory by looking at how the governance of data, information, knowledge and wisdom supports the usefulness of information technology within organizations. Data governance "consists of strategy, organization and roles, policies and standards, projects and services, issues, and valuation that require an integrated management life cycle of data architecture, data quality, metadata, document and content, data warehousing, reference and master data, data security, database operations, and data development" (p. 2). Information governance "plans, builds, runs and monitors the practices, projects and capabilities that acquire, control, protect, deliver and enhance the value of data and information assets, in alignment with the direction set by the data and information governance body" (p. 4), while knowledge governance is "maintaining the availability of relevant, current, validated and reliable knowledge to support all process activities and to facilitate decision making and planning for the identification, gathering, organizing, maintaining, use and retirement of knowledge" (p. 6-7).

Efe (2017) goes on to describe how wisdom fits into this matrix of data, information and knowledge governance. "Wisdom allows decision makers to visualize opportunities or see problems in a new light to cut to the heart of the relationships to determine what needs to be done. Essentially, wisdom is a vital organizational resource accumulated through experience and applied to everyday learning at work. Wisdom can be used as an organizational strategy to realize the real potential of any organization, particularly the relationship of wisdom with organizational goals, governance and stakeholder needs for which value-adding processes need to run in conformity with the goals cascade" (p. 3).

There has been consideration about whether machines and systems can ever develop wisdom through machine learning and artificial intelligence. Most support the notion that human wisdom is critical to this process. "To date, computers do not display tacit knowledge and may never be able to fully embody wisdom. However, the attainment of wisdom can be supported by computer systems. A computer system can be programmed with information and knowledge to promote human learning, understanding, and personal growth, which, in turn, support the development of human centered wisdom. Wisdom is an attribute of human beings. Thus, even though computers do not themselves possess wisdom, nurses who use computers can exhibit wisdom in their practices and can develop wisdom through the use of computer systems. Nursing informatics specialists support developing wisdom through sound management and communication of data, information, and knowledge. The appropriate and ethical application of knowledge and theory is understood as wisdom" (Matney et al., 2011, p. 16).

The DIKW theory is just that - a theory. However, it does help nurses and other professionals to articulate how we use data, information, and knowledge within our practice and how they can support the development and application of wisdom within the context of healthcare. "Alternatively, perhaps DIKW can be viewed as a vision or goal for nursing informatics: a depiction of nursing informatics as a vehicle in the movement toward nursing praxis. In this view, we would then be directed to taking up DIKW as a means of understanding nursing informatics, inclusive of both computerized clinical information systems and nurses as users, since nursing praxis would require both components" (Ronquillo, et al., 2016, p. E.16). The theory helps to articulate the technological aspects of nursing informatics while creating a wide space for the ingenuity of the human nurses themselves.

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Theory applied to informatics: DIKW Theory

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by June Kaminski, RN MSN PhD(c) Editor in Chief CJNI was initiated by June Kaminski in 2006 when she was President-Elect of CNIA. She is currently Faculty and Curriculum Coordinator of a BSN Advanced Entry nursing program at Kwantlen Polytechnic University; Communications Officer, Webmaster, and former President of the Canadian Association of Nurses for the Environment and Editor in Chief of the Online Journal of Nursing Informatics.