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Data analytics has played a critical role in healthcare, especially during the Covid-19 Pandemic. It has improved healthcare through the allocation of healthcare resources and the use of predictive models to predict surges of coronavirus.

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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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The Fundamentals of Predictive Analytics - Data Science Wednesday

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"In order to protect consumer health data so that consumers and health professionals can leverage the power of data in the digital age, revisions to the current privacy laws are vital"

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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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5 easy ways to engage patients using your EHR patient portal

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A delay in the explanation or delivery of results can lead to "the difference between a treatable problem and an inoperable one".

"Every hour matters"

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By Brian Ward

In December, The Joint Commission (TJC) released Quick Safety Issue 52: Advancing Safety With Closed-Loop Communication of Test Results. Closed-loop communication means that every test result is sent, received, and addressed in a timely manner, and the patient is notified of the results and next steps.

"Diagnostic error is one of the most important safety problems in health care today. It is among the most common, catastrophic, and costly of serious medical errors and one study estimates that approximately 1 in 20 U.S. adults will have a diagnostic error annually in the outpatient setting," according to Quick Safety Issue 52.

Serious problems can occur when results are delayed. Patients can undergo the wrong operation, be prescribed the wrong medication, or be discharged when they actually require urgent care. A delay in test results can mean the difference between a treatable problem evolving into an inoperable one. For example, sepsis is the main cause of one in three hospital deaths. Any delay in administering antibiotics to a patient with sepsis increases mortality by 10%, so every hour matters. If someone in the lab delays sending a test result until after lunch or a clinician doesn't act on it until the end of the day, there can be an immediate and negative impact on a patient's condition.

"The Joint Commission identified the timely reporting of results of critical tests and diagnostic procedures as a National Patient Safety Goal in 2005 (NPSG.02.03.01). However, the implementation has been inconsistent to date and this goal does not necessarily address the risks related to communicating 'sub-critical' test results that are not immediately life-threatening and are often communicated through nonverbal channels," says the report.

Sue Sheridan, MIM, MBA, DHL, director of patient engagement at the Society to Improve Diagnosis in Medicine, and Hardeep Singh, MD, MPH, chief of Health Policy, Quality & Informatics Program at the Center for Innovations in Quality, Effectiveness and Safety, wrote a blog post on The Joint Commission website about closed-loop communication.

"A systematic review reported that 6.8 to 62% of laboratory results and anywhere from 1 to 36% of radiology results are not communicated, which sometimes leads to missed cancer diagnoses. This occurs despite patients' preferences for having all results-normal and abnormal-communicated to them. Workflow factors and confusion about who is responsible for following up with patients can lead to some of these communication breakdowns," they write.
EHR
With the advent of electronic health records (EHR), communication of results can be faster and more reliable. However, building a quality EHR system takes work, and systems still have to contend with nontechnical problems such as:

User behaviors

Usage practices

Policies and procedures related to communication and follow-up

Training issues

Organizational practices

Workflow-related issues

It was found that almost 8% of EHR notifications regarding abnormal outpatient test results don't receive follow-up for four weeks.
Patients and portals
A major part of closing the communication loop is making sure that patients not only receive their results, but understand what they mean. Without engagement, it's difficult for patients to understand what they have to do next. Do they need a follow-up appointment? Do they need to keep taking the medication last prescribed? Are they cleared to go whitewater rafting over the weekend, or do they need a few more days of rest?

"Failure to close the communication loop is also among the contributing factors for high-severity medical malpractice claims," write Sheridan and Singh.

Another tool for delivering test results is patient portals, websites where patients can log in to see their medical records and history. However, fewer than a third of a patients use these portals, and a recent study suggests portals are "not currently designed to present test results to patients in a meaningful way."

Providing a patient portal is pointless if it's not updated regularly with the patient's latest results or if it's challenging for the patient to navigate.
Recommendations
TJC has several suggestions on how to close the loop on test results, including:

Identify vulnerable workflows.

Find the weak points in the flow of test results and develop fixes and backups.

Establish consistent processes.

Like all patient safety topics, quality necessitates consistency.

Notify patients of life-threatening test results.

Results like these should be verbalized to the patient, with a system in place to ensure that the information was received.

1. Forward abnormal test results to an alternate responsible provider if results are unacknowledged for a set time.

2. Send all test results (abnormal or normal) to a backup provider in a timely fashion if the ordering provider is unavailable.

The appropriate timeliness should be based on the significance of the test result.

Optimize your organization's electronic systems for communicating test results.

You can automate some of the aforementioned processes this way.

Make your patient portal(s) more user-friendly.

Many patients find portals confusing. Sometimes this is because of how they are designed, but sometimes it's because people don't have the context to understand their test results. Some issues you can address are:

Ensure the portal works on desktop and mobile devices

Make the patient's EHR information accessible on the portal, preferably with real-time clinical notes and test results

Explain test results in terms the patient understands

Explain what the results mean for the patient, including what steps to take next

Improve access to support services that help patients act and follow up on their results

Set standards for updating test results on the portal

No matter how good your portal is, TJC warns to never assume patients will use it. Some might be uncomfortable or uncertain of using an online portal, prefer person-to-person contact, or lack regular access to the internet. Portals should especially never be your sole method of communicating abnormal test results-reach out directly to patients when action is needed.
A view from the lab

Dan Scungio, MT(ASCP), SLS, laboratory safety officer for multihospital system Sentara Healthcare in Virginia, and otherwise known as "Dan, the Lab Safety Man," shares his thoughts on Quick Safety 52.

"Many labs have much of what is being discussed here in place already and call in critical values (results that need fast action) to physicians or nurses as soon as possible. This can be more difficult for outpatients, especially when offices are closed. If a physician cannot be reached, the situation may be escalated to the lab pathologist. Sometimes they may need to contact the patient directly (which lab staff would never do) and provide instructions to get immediate follow-up or even go to the hospital.

Labs have had to call in critical values for years, and many have specific ways in which they provide information and document the call.

Patient portals are generally not helpful for critical values situations, despite many now providing results to patients fairly quickly. But labs cannot rely on portals as patients may not look for results, may not understand results, and may not follow up."

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Close the Loop on Test Results - Patient Safety & Quality Healthcare

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Serious problems can occur when results are delayed. Patients can undergo the wrong operation, be prescribed the wrong medication, or be discharged when they actually require urgent care. A delay in test results can mean the difference between a treatable problem evolving into an inoperable one.

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Is your data ethically sourced?

So you're ready to collect some data and start modeling, but how can you be sure that your data has been ethically sourced?

CW: I talk about mental health and suicide prevention in a section below.

Giammarco Boscaro on Unsplash

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So you're ready to collect some data and start modeling, but how can you be sure that your data has been ethically sourced? CW: I talk about mental health and suicide prevention in a section below.

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University of Toronto Family & Community Medicine

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Technology in Health: Privacy, ethics & health data

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What is the difference between Ethics, Morality and the Law?

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Data visualization has led to many improvements in healthcare including:
1. patient education
2. coordination of care
3. documentation of health trends (especially during the covid-19 pandemic)

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Benefits of Data Visualization for Presenting Health Data | Ohio University

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Are you curious about data analytics and visualization? Learn the benefits of data visualization and explore real-world applications for your organization.

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Humans benefit most from well presented visual data, pressing the need to clear and concise data visualization

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Boston hospital alarm fatigue technique held up as national model

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I don't love the beginning sentence of this article as it makes healthcare workers appear to be disinterested in patient safety (which we know is not the case!) but the article goes into a pilots comparison of aircraft error to hospital error in a very compelling way.

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What Pilots Can Teach Hospitals About Patient Safety (Published 2006)

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A growing number of health care providers are trying to learn from aviation accidents and from what the airlines have done to prevent them.

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Swiss cheese effect leads to hazards and harm to patients even with the use of informatics and EHRS

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Diabetes plate method

1. Fill half your plate with nonstarchy vegetables

2. Fill one quarter of your plate with lean protein foods

3. Fill one quarter of your plate with carbohydrate foods

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The Diabetes Plate Method is the easiest way to create healthy meals that can help manage blood sugar. Using this method, you can create perfectly portioned meals with a healthy balance of vegetables, protein, and carbohydrates-without any counting, calculating, weighing, or measuring. All you need is a plate!

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"“Mobile technology and games can be powerful tools to support and empower patients,” said Scott Josephs, M.D., Cigna national medical officer."

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Games, Tech Keep Young Cancer Patients Connected

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Cigna has teamed up with the Childhood Leukemia Foundation to provide to patients free iPads preloaded with cancer-fighting game app Re-Mission 2: Nan

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Cutting edge science combined with a game company in order to better understand the diagnosis and progression of dementia.

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The COVID-19 pandemic is accelerating changes already underway in the practice of medicine, spurring both innovations and growing pains around technologies that allow patients to interact with the healthcare system virtually.

An April survey conducted by Sermo, a physician online community, reported that 85% of participants are seeing patients via video and phone. The survey also found that 68% of respondents believe this will have a lasting effect on how doctors see patients, and 77% support the shift to telemedicine. The telemedicine market is expected to be a $155 billion industry by 2027, according to Grand View Research.

This drive to see patients at home has also increased interest in at-home sample collection and testing, both for convenience and to avoid potential exposure to SARS-CoV-2, the virus that causes COVID-19 illness. "COVID-19 has accelerated this," said Arielle Trzcinski, a senior analyst at Forrester, a market research company. She anticipates "a greater push on how we get to more convenient, cost-effective options for lab testing. Some vendors are already working on that, but it's still an area that is further ripe for disruption and innovation."

Pandemic Concerns Speed Up Market for Home Testing

As the pandemic bore down on the healthcare system and the economy, companies began moving to bring direct-to-consumer (DTC) testing services online. Now, more steps of the testing process are entering patients' homes, from test ordering to sample collection. Patients themselves might even perform certain tests.

On April 21, the Food and Drug Administration (FDA) authorized LabCorp's Pixel for COVID-19, the first at-home sample kit for SARS-CoV-2 testing, specifically for LabCorp's COVID-19 reverse transcriptase polymerase chain reaction test. The self-collection kit contains nasal swabs, saline, and an insulated package for sending the samples back to LabCorp.

Of course at-home testing isn't new, especially when it comes to collecting cheek swabs and saliva. Consumers already have enthusiastically embraced kits marketed for allergies, vitamin D, food sensitivity, and DNA testing-for humans and pets. But most of these tests report genetic traits or conditions that "are not going to necessarily indicate a life or death situation," said Trzcinski.

Telemedicine startups Vault Health and hims & hers, and DTC genetic testing company Vitagene, are now all selling home-collected saliva-based test kits. The actual testing for all three companies is performed by Rutgers University laboratory, called RUCDR Infinite Biologics, in partnership with Spectrum Solutions and Accurate Diagnostic Labs. FDA authorized the RUCDR test on May 7, and prices range from $116-$150.

Scanwell Health, a developer of smartphone-enabled, at-home diagnostics, has announced its work on a test kit ordered from and conducted at home. Scanwell has exclusive rights to license and distribute a SARS-CoV-2 rapid serology test from Chinese company Innovita. The test takes 15 minutes to complete using the Scanwell Health mobile app, and under the company's plan, a doctor or nurse practitioner would reach out within hours to a patient with results and next steps. Scanwell is known for developing the first urinary tract infection test cleared by FDA for home use.

Most recently, DTC company LetsGetChecked received FDA authorization for its Sure-track test that includes at-home nasal swab sample collection and lab-based molecular testing for SARS-CoV-2. The company touts that it is the only DTC offering with an FDA emergency use authorization for at-home COVID-19 testing that owns all aspects of the testing service, including the collection kit manufacturing process, logistics, lab analysis, and physician approval.

Demand has also spiked for SARS-CoV-2 antibody and antigen tests that are easier to accomplish with small blood samples compared to the nasopharyngeal and nasal swabs used for lab-based molecular diagnostic assays.

In late April, Quest Diagnostics announced it would start selling a COVID-19 antibody test directly to consumers for $119. This test still requires a blood draw at one of the company's patient service centers, and the utility of antibody tests remains unclear. This caveat hasn't stopped consumers from purchasing the test (Quest said it had performed 975,000 antibody tests as of May 18), reflecting demand for ways to take blood samples without risking COVID-19 exposure and for a testing process anchored to consumer convenience.

Advances in Sample Collection Arrive at an Opportune Moment

Innovations in blood sample collection are proving their utility and validity just in time for the home-based medicine push. One such system is Neoteryx's Mitra devices, which collect 20 mL of blood from a finger prick. Cathy Cordova, Neoteryx's director of marketing, said Mitra is already used by organ donor transplant recipients, who need frequent blood tests to check their immunosuppression. Home testing lets them avoid coming into a healthcare setting repeatedly for blood draws, and makes frequent testing easier for those who live in rural areas, far from hospitals and clinics.

A study recently published in The Journal of Applied Laboratory Medicine found that 82% of patients preferred using this microsampling device for therapeutic monitoring of tacrolimus and cyclosporine immunosuppressants following solid organ transplants (J Appl Lab Med;2020;5:516-30).

Neoteryx has also worked with "several large pharmaceutical companies that have looked to implement their phase I, II, and III clinical trials for drug development through what they're calling virtual clinical trials," Cordova said. These patients can participate while taking their own blood samples from home.

Mitra will also be used for a National Institutes of Health (NIH) COVID-19 serosurvey, a nationwide COVID-19 antibody survey that researchers hope will help determine how many adults without a confirmed history of COVID-19 virus infection already have antibodies. NIH plans to test as many as 10,000 volunteers, who will be shipped Mitra kits to take their own blood samples at home.

"Unfortunately, I think it's taken the pandemic to really put a spotlight on the value of remote sampling," Cordova said.

Researchers also are showing that with the right technique and instruments, an old technology-dried blood spots-can be updated to offer lab-quality results for many analytes. According to authors of a paper published in Clinical Chemistry, "broader adoption of these specimen types should provide opportunities for more efficient individualized health monitoring with an overall objective of improved health and wellness" (Clin Chem 2020;6:821-31).

Potential Hurdles for Clinical Laboratories

For clinical laboratories, the ramp-up of at-home testing isn't necessarily straightforward, and the experience of those who've worked with some of the new technologies suggests labs will face speed bumps along the way. The first is ensuring that a sample is taken correctly. "You need really clear instructions on how to do the test properly," Trzcinski said. For example, over- or under-saturating microsamplers with blood could ruin the test. Second, users of these tests also need information on how to interpret their results. Telemedicine is being deployed to solve both these issues.

For COVID-19 testing, AACC has asked FDA to ensure robust scientific evidence and transparency around home sample collection. Unique preanalytical problems often surface with self-collection methods that can put patients at risk, the association emphasized in a letter to the agency.

"While home sample collection kits are designed to be simple, problems commonly occur with self-collection that can affect the quality of the sample and, therefore, the subsequent test result," the letter said. Moreover, in the case of LabCorp's Pixel test system, the experiments the company conducted showed stability of live virus that had been spiked onto swabs. "But no experiments were publicly shared that demonstrate equivalence between specimens collected by healthcare professionals and specimens collected by the patients themselves at home," said AACC's letter. LabCorp declined to comment for this story.

Laboratories themselves likely will face mounting challenges when asked to process samples taken from home kits. Last year, Kamisha Johnson-Davis, PhD, DABCC, FAACC, associate professor of pathology at the University of Utah and medical director for clinical toxicology at ARUP Laboratories in Salt Lake City, ran a feasibility study on the Mitra device. The research team specifically looked at using the device for immunosuppressant drug monitoring (J Appl Lab Med 2019;4:241-6).

Johnson-Davis raised several issues about the device, including the potential to over- or under-saturate the Mitra tip. Her laboratory team also had to figure out how to fit the Mitra device into their workflow so that it could be tracked just like any other sample. The shape of the Mitra device also posed a complication, she said: The one they tested is clamshell-shaped, "which is totally different from the collection tubes that we use."

Johnson-Davis also had concerns about the quality of the samples themselves. "As a medical director interested in these alternative ways of collecting specimens, it would be of interest to understand how this would work in practice in someone's home," she said. Laboratory directors would need to know how they can be sure of the sterility of the sample-taking environment, if patients are milking their fingers, and what happens to a sample between the time it's taken and when it shows up at a laboratory.

"In practice, we just don't know how patients are really going to transport samples," said Johnson-Davis. "Let's say it's summertime and they forget they had it in their car but put it in the mail anyway. What's going to happen to that specimen?"

Transportation logistics extend to laboratories too, she said. Maybe a laboratory doesn't receive mail on Saturdays. Or a patient puts a sample in a mailbox on a Saturday afternoon but it isn't picked up until Monday.

"There are things we would need to work out in regard to specimen processing on-site and having it accommodate our track system," she added. Plus, "we would need communication and videos on appropriate specimen collection and transport for the clients who would potentially be using these."

In all of these remote sampling scenarios, clinical laboratory professionals have an opportunity to lead-from discovering how technologies and techniques work in the real world to developing the right quality systems that ensure high-quality results for patients, regardless of where sample collection takes place.
Jen A. Miller is a freelance journalist who lives in Audubon, New Jersey. @byJenAMiller

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COVID-19 Is a Catalyst for Remote Sampling and Telemedicine

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The COVID-19 pandemic is accelerating changes already underway in the practice of medicine, spurring both innovations and growing pains around technologies that allow patients to interact with the healthcare system virtually. An April survey conducted by Sermo, a physician online community, reported that 85% of participants are seeing patients via video and phone.

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How telehealth has benefitted mental health diagnosis, care and treatment through increased accessibility

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Mental Healthcare Online: Options in Telehealth

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How the Pandemic influenced the progression of telehealth

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Telehealth Is Booming During Covid Pandemic. Is it Here to Stay?

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HIE prevents fragmented incomplete medical information to improve quality of care and patient outcomes.

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Showing How a Health Information Exchange HIE Works Using Animation

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According to the above source benefits of HIE includes:
- improving patient safety
- increased efficiency
- improved quality/outcomes
- improve public health reporting
- cost reduction
- provides clinical decision making support tools

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To better track and securely share patients' complete medical histories, more and more health care providers are participating in health information exchange ( HIE). HIE helps facilitate coordinated patient care, reduce duplicative treatments and avoid costly mistakes. This practice is growing among health providers because the need for HIE is clear and the HIE benefits are significant. A few examples of HIE benefits include:

Improve patient safety by reducing medication and medical errors;

Increase efficiency by eliminating unnecessary paperwork and handling;

Provide caregivers with clinical decision support tools for more effective care and treatment;

Eliminate redundant or unnecessary testing;

Improve public health reporting and monitoring;

Engage healthcare consumers regarding their own personal health information;

Improve healthcare quality and outcomes; and

Reduce health related costs.

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What are the benefits of health information exchange?

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To better track and securely share patients' complete medical histories, more and more health care providers are participating in health information exchange ( HIE). HIE helps facilitate coordinated patient care, reduce duplicative treatments and avoid costly mistakes. This practice is growing among health providers because the need for HIE is clear and the HIE benefits are significant.

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In the state that I practice in, Connecticut, Connie is the Health Information Exchange that was established in 2019 and put into practice in May of 2021. This is a non-profit source that does not charge providers to receive patient data from them.

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Connie, CT’s health information exchange, is a safe, confidential way of sharing health information digitally among healthcare organizations.

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Showing how the use of EHR was used to improve care in patients with diabetes, decreasing hospital stays and emergency room visits.

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Electronic Health Records Linked to Improved Care for Patients With Diabetes

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"School of Pharmacy Professor Michelle Chui publishes the first study on the implementation of CancelRx, which showed a 93% success rate in canceling prescriptions from the clinic to the pharmacy at UW Health"

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Using Technology to Improve Patient Safety, Health IT Tool Significantly Reduces Medication Over-Prescription - School of Pharmacy

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School of Pharmacy Professor Michelle Chui publishes the first study on the implementation of CancelRx, which showed a 93% success rate in canceling prescriptions from the clinic to the pharmacy at UW HealthBy Katie Ginder-VogelWhen a doctor stops or changes a prescription in a patient’s electronic

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touches upon the pros and cons of patient portal communication among both the patient and provider

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Top Tips for Successful Patient Portal Secure Messaging

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Patient portal secure messaging is an important patient engagement technology. Clinicians need to reframe workflows to successfully implement messaging.

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a great quick video summarizing meaningful use in a simplistic way

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https://www.healthitoutcomes.com/doc/powerful-benefits-of-e-prescribing-0001

Prior to e prescribing, errors in hand written prescriptions lead to approximately 440,000 deaths/year

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By Kayla Matthews, Productivity Bytes

Hand-writing prescriptions can be inefficient and introduce medication error, which can be costly or even dangerous for patients. It's estimated to be responsible for up to 440,000 deaths each year. Some of these mistakes can be reduced with advanced technology, including e-prescribing.

E-prescribing allows physicians and other medical professionals to send prescription information to pharmacies digitally. They also can store data about that RX as part of a larger electronic health record system.

These systems are being adopted as part of a push to improve the safety and reliability of prescriptions, but there are other advantages they can offer.

Here are six powerful benefits of e-prescribing.
1. Reduce Pharmacist Error
Compared to hand-written prescriptions, e-prescriptions are extremely clear in terms of treatment and dosage and require less interpretation on the part of the pharmacist. This can reduce prescription and medication errors. It also can help ensure patients have the most accurate information possible about how a particular drug should be taken.

E-prescriptions can reduce the risk of an incorrect dose, which can easily waste time or even cause harm to a patient in a worst-case scenario. One meta-study found that when practices implemented an e-prescribing system, medication error could be reduced to one-seventh its original levels.
2. Instant Clinical Alerts
In the United States, more than 4.5 million visits to ERs and physicians' offices are caused by preventable adverse drug events (ADEs).

These ADEs can be caused by allergies, interactions with other drugs or treatments, and other factors - like high dosages and chronic use of specific medications. Many e-prescription systems are designed to interface with electronic health records (EHRs) that aggregate information about patients' current and past medical history, including allergies. These systems can then alert clinicians if a prescription may interact with another treatment or prevent them from prescribing a medication that a patient would be allergic to.

Many e-prescription systems also will offer alerts about duplicate therapies, which can help doctors avoid prescribing multiple treatments for the same medical indication.
3. Prescription Fulfillment Tracking
E-prescription systems can help doctors track whether or not patients have picked up the next refill. In this way, these systems can let clinicians know which people are staying on top of their medication and which may have stopped following a prescribed treatment.

With this information, clinicians follow up with patients who haven't filled their prescriptions to help them get back on track. This practice can help to reduce readmissions and ensure patients are following through on courses of treatment that need to be completed - like antibiotics.
4. Increased Prescription Efficiency
Hand-writing prescriptions can be both tedious and costly in terms of time spent, as they often need to be individually drawn up and authorized. With some e-prescription systems, clinicians can automatically refill dozens of RXs at the same time, allowing them more to review them. This can both increase efficiency and reduce the prescription error rate.

Clinicians also can quickly authorize an emergency refill in cases where patients have lost access to their medication. This could happen when someone is traveling and has left their medicine at home.
5. Reduce Readmissions
Every year, there are more than 1.6 million hospital readmissions, and more than 13 percent of them are caused in part by adverse reactions to drugs. Of these, 95 percent were due to inappropriate prescription practices - including failure to consider potential side effects and interactions.

When integrated with records systems that provide access to information about a patient's allergies and drug history, e-prescription systems can reduce errors.

They also can give clinicians better information about potential interactions and side effects that they can use to inform their prescription practices. Clinicians that work with e-prescription systems can expect to reduce patient readmissions, saving both doctors and patients time and money.
6. Cost Savings
All these advantages also can result in cost savings for practices that adopt e-prescription systems.

The benefits of e-prescription - including improved outcomes, increased efficiency and reduced patient visits - have been shown to save practices between $140 and $240 billion over the last 10 years. The amount of money saved may even increase in the future as e-prescription systems advance and as pharmacies grow more accustomed to handling them.
How Practices Can Benefit From E-Prescribing
The prescription process can allow several errors to creep in - in dosage, treatment instructions and even the medication itself. E-prescription systems can help reduce the rate of prescription and medication error by automatically providing clinicians with relevant patient information and by giving clearer prescription information to pharmacies.

The primary benefit of e-prescription systems is in increasing the safety and efficiency of prescribing practices - but they also offer several other benefits. Studies of e-prescription have found that these systems can save time and money, while also improving patient outcomes. That makes it well worth looking into.
About The Author
Kayla Matthews is a MedTech writer whose work has appeared on HIT Consultant, Medical Economics and HITECH Answers, among other industry publications. To read more from Kayla, please connect with her on LinkedIn, or visit her personal tech blog at https://productivitybytes.com.

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Hand-writing prescriptions can be inefficient and introduce medication error, which can be costly or even dangerous for patients. It's estimated to be responsible for up to 440,000 deaths each year. Some of these mistakes can be reduced with advanced technology, including e-prescribing.

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https://www.youtube.com/watch?v=eVSfJhssXUA

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https://www.youtube.com/watch?v=Z7QPZpBzmxw

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Why Medical Terminology is Important in the Healthcare Field

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https://www.healthitoutcomes.com/doc/the-importance-of-standardized-healthcare-terminology-0001

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Get the best of Health IT Outcomes delivered straight to your Inbox! sign me up

Article | October 24, 2016

By Cheryl Mason, Health Language
In our previous blog, we discussed the importance of leveraging administrative data for better quality assessment. As the wrap-up to this standards blog series, I want to look back at Crossing the Quality Chasm as a foundational work for improving the quality of healthcare delivery. This paper called not only for better quality but also for a reduction in the cost of that care--this at a time when the population is aging, technology is evolving, and research is rapidly expanding evidenced-based medicine. At least eight of the 13 recommendations made by the authors of Crossing the Quality Chasm directly involve the collection, aggregation, and actionable use of healthcare data. The remaining five are supporting those objectives through the development of committees that address quality care and reimbursement models, and in training the workforce to meet the increasing needs of an industry that is becoming more and more reliant on data and analytics.

We introduced the major standard terminologies needed to help collect data in a structured way so that we can measure quality, meet meaningful use requirements, and provide accurate information at the point of care. We also touched on value-based care models and how standards such as SNOMED, LOINC, and RxNorm can help your organization aggregate and analyze the data that is found in disparate systems across your enterprise to meet the needs of better reporting and quality measures. We talked about billing and coding standards (ICD-9, ICD-10, and CPT), SNOMED for documenting problem lists in the EHR, LOINC to code laboratory data, RxNorm for pharmaceutical information, and the importance of administrative data like race and ethnicity.

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The Importance Of Standardized Healthcare Terminology

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In our previous blog, we discussed the importance of leveraging administrative data for better quality assessment. As the wrap-up to this standards blog series, I want to look back at Crossing the Quality Chasm as a foundational work for improving the quality of healthcare delivery.

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A Proposal for Health Informatics and Data Science

Abstract

In this commentary, I revisit and modify Ackoff's data-information-knowledge-wisdom (DIKW) hierarchy. I suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge (DIEK). This framework defines data as raw symbols, which become information when they are contextualized. Information achieves the status of evidence in comparison to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by success and consensus. As checkpoints for the transition from evidence to knowledge I suggest relevance, robustness, repeatability, and reproducibility

Keywords: Data, Information, Evidence, Knowledge

Introduction

Data, information, and knowledge are central concepts in health informatics and data science. It is not always clear how authors define these entities and how they envision the transition from data to knowledge to work. In this commentary, I first review the knowledge/wisdom hierarchy proposed by organizational theorist Russell A. Ackoff in 1989 [ 1]. Second, I outline a modification of Ackoff's framework that does away with his notion of wisdom and makes room for evidence. I also discuss the transition process of from data to knowledge, with a focus on the transition from evidence to knowledge. I hope that the ideas summarized here will prove helpful to those in charge of knowledge generation in health informatics and data science.

Ackoff's Knowledge Hierarchy

Russell L. Ackoff (1919-2009) introduced what is now known as the knowledge hierarchy or knowledge pyramid ( Fig. 1 , left) in his presidential address to the International Society for General Systems Research (ISGSR) in 1988 [ 1]. He starts with the notion that wisdom is situated at the top of a hierarchy of types of content in the mind, followed by understanding, knowledge, information, and data ( Fig. 1 ; of note, Ackoff's original article does not have a figure, nor does it refer to pryamids.) He defines data as symbols that are properties of observables, and information as descriptions. The difference between the two is not structural, but functional, and information is inferred from data.

Figure 1

Ackoff discusses management needs in terms of information availability. He states that managers are usually confronted with an information overload and do not necessarily need more relevant information but less irrelevant information, a truism then and now. He defines knowledge as know-how that comes from learning, i.e., by instruction or from experience, and adaptation, i.e., the correction of the learned in accordance with new circumstances. This process requires understanding what error is, why error occurs, and how to correct it. Ackoff thinks that (1) information systems can be automated and generate information out of data, (2) that computer-based knowledge systems require higher-order mental faculties; "they do not develop knowledge, but apply knowledge developed by people", and (3) that wisdom adds value, endures forever, and will probably never be generated by machines.

Ackoff's hierarchy is often depicted as a pyramid (as in Fig. 1 in this article) with data at the bottom, information and knowledge above, and wisdom at the top. Probably for this reason, Jennifer Rowley uses the term "wisdom hierarchy". [ 2] Although she seems more interested in the wisdom part than in other components of the pyramid, the bulk of her 2007 paper on Ackoff's work is a summary of terminological definitions, of data, information, knowledge, and wisdom, as pulled from major textbooks used in information system and knowledge management education. Her review reiterates two opinions; first, her view that data, information, and knowledge are connected, one helping define the other, and second, her view about the organization of the hierarchy as such. The ways how the individual items in the hierarchy are converted and elevated to the next level is less well defined.

Data, Information, Evidence, Knowledge: DIEK

Although the Ackoff hierarchy has received much attention over the years, I strongly believe that in our current evidence-based environment some modifications are in order. First, in a book co-authored with philosopher of science Ben Smart, I suggest dropping the notion of wisdom because, first, the term is fraught with too much baggage from non-scientific context [ 3]. Second, Ackoff's definition of wisdom (the addition of value to knowledge that requires judgement) ignores the fact that judgement is needed at all levels of the hierarchy. More importantly, 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.

Data

In the context of Public Health Informatics, Mensah and Goderre define "data" as raw facts, statistics, context-free numbers [ 4]. I'd like to suggest that data are symbols as retrieved, collected, or simulated ( Table 1 ). These include numbers resulting from measurements or from text-mining, images, sound recordings, survey results, simulations, and so on. They can usually be tabulated and depicted as graphs, or displayed as figures. More formally speaking, data are quantitative or qualitative values of variables. Figure 2 displays a framework for transitions from data to knowledge, and what the arrival at each new stage is good for.

Table 1

Concept

What is it?

How produced?

By whom?

Goal?

Data

Numbers, Symbols, Text, Images, Sound recordings, Unit values

Collected from field research, database, measurements in experiments, from individuals, populations

Data Collector

Use as raw data or for information generation
Storage, curation, retrieval

Information

Data in context

Contextualization by making data useful, and using them, for specific tasks

Informatician, informaticist, statistician, data scientist

Use as source for answering questions
Storage, curation, retrieval

Evidence

Useful, contextualized information

Comparison with standards, reference values, reference information

Scientist,
theoretician,
philosopher
Interventionist, policy maker

Use for analysis and hypothesis-testing to support claims/hypotheses and decision-making

Knowledge

Evidence-based, (predictive, testable, consistently successful) belief

Consensus based on reasoning and discussion

Justification

Figure 2

Information is data contextualized

Mensah & Goderre further suggest that "information is the collection, aggregation, analysis, and presentation of data that provides understanding". [ 4] Although this definition describes how we arrive at information based on data, it does not tell us what information is. I think that information is data in context. Information is data that have been processed so it is clear what they are about. Once they are collected and contextualized, data are information. According to this view, all information is data, but not all data are information.

Evidence is information compared

Information thus conceived can give rise to evidence, which has been defined as "information bearing on the truth or falsity of a proposition". [ 5] Evidence is information that can be used to support a hypothesis by testing it. Thus, all evidence is information, but not all information is evidence. The comparison of information in support of competing conjectures helps define what counts as evidence that, in turn, generates the knowledge that a certain overarching claim is true.

Evidence is generated by comparing information to reference values or standards, which prepares the information for further analysis. In the context of public health, Brownson and colleagues have argued that

(f)or a public health professional, evidence is some form of dataâ€"including epidemiologic (quantitative) data, results of program or policy evaluations, and qualitative dataâ€"for uses in making judgments or decisions" [ 6]

They describe three kinds of evidence in public health contexts: (1) the causes of illness and the magnitude of risk factors, (2) the relative impact of specific interventions, and (3) how and under which contextual conditions interventions were implemented [ 6]. We discuss the intervention-related part of these kinds of evidence in more detail elsewhere [ 7].

In general, evidence is information that bears on the truth of a proposition compared to a standard. According this definition, information becomes evidence only if it bears on the truth or falsity of the proposition that the gardener was indeed the murderer. Only if we can find good evidence that is coherent with this claim can we say that we have knowledge that he really is the culprit. Actionable knowledge is usually generated from coherent evidence from multiple independent sources of information [ 8]. If we refer to evidence as information that supports a specific proposition by bearing on its truth, evidence is context-dependent, because it becomes evidence only by virtue of being relevant as support for a specific proposition, and relevance is, by definition, a contextual concept.

Knowledge from evidence

The traditional tripartite concept of knowledge as justified, true belief goes all the way back to Plato [ 9]. Gettier argued in 1963 that the tripartite definition is not sufficient to constitute knowledge, in essence by offering two counterexamples in which some justified, true beliefs clearly do not count as knowledge [ 10]. Multiple strategies to defeat Gettier have been suggested [ 11]. In our present context, I think that knowledge consists of beliefs that

1. turn out to be predictive: predictions that are based on such beliefs turn out to be correct;

2. generate hypotheses that can be tested, and

3. ideas that lead to interventions that are successful,

4. for a long time.

In other words, I suggest that beliefs qualify as knowledge if they predict outcomes with satisfactory precision, if they can be translated into scenarios that put the belief to the test, and if actions based on such beliefs are consistently successful. In short, knowledge is predictive, testable, consistently successful belief. Indeed, this is exactly what we refer to some belief as being evidence-based. This is why evidence-based medicine and public health should actually be considered knowledge-based once the evidence has turned out to be predictive, is tested, and interventions have been designed and are consistently successful. Of course, the decision when that point has been reached is not made by any one person, but by consensus [ 12, 13]. Thus, all knowledge is evidence, but not all evidence is knowledge.

Are there checkpoints that support the decision to promote evidence to the level of good before we have seen the quality of its predictions, witnessed its testability, and received the good news that interventions based on such evidence are being consistently successful? Here is a collection of candidate checkpoints that I think allow us to proceed from evidence to knowledge. Since we ask this question with an intervention in mind, our query is not really what makes evidence so good that it is knowledge, but rather what makes evidence so good that it is useful in our context. Usefulness, in turn, is simply the possibility to use this knowledge in ways that turn out to help improve the health of individuals and populations. We need knowledge to justify action.

First, although this should go without saying, good evidence is relevant to the problem at hand. Consider this quote from the Annual Review of Public Health:

Legislators and their scientific beneficiaries express growing concerns that the fruits of their investment in health research are not reaching the public, policy makers, and practitioners with evidence-based practices. Practitioners and the public lament the lack of relevance and fit of evidence that reaches them and barriers to their implementation of it [ 14]

If evidence is irrelevant, it isn't useful. The focus on usefulness is, yet again, motivated by the goal of health informatics efforts to inform decision making which leads to effective action. Second, good evidence is robust. This is what Broadbent has called the stability of a result, i.e., the characteristic of a theory or piece of evidence that it is (a) not soon contradicted by good scientific evidence, and (b) unlikely that it will soon be contradicted by good scientific evidence, if good research were to be done on the topic [ 15]. Third, good evidence is repeatable in the sense that similar data gathering and integration efforts lead to similar evidence repeatedly: "Repeatability concerns the exact repetition of an experiment, using the same experimental apparatus, and under the same conditions". [ 16] Fourth, good evidence is reproducible: "Reproducibility is â€¦ implementing the same general idea, in a similar setting, with newly created appropriate experimental apparatus". [ 16]

Conclusion

My version of the Ackoff hierarchy is based on what is being done to make such transitions possible, not what transitions represent or what happens when moving from one level to another, such as changes of meaning and value [ 17] or the physical, cognitive, and belief structuring when constructing data, information, and knowledge, respectively [ 18]. As Rowley's focus is on the relative paucity of explications of wisdom, mine is instead on the fact that the concept of knowledge, now at the top of the hierarchy, is not well defined either.

A similar model has been proposed by Richard Heller. In his model, accessing data yields information, appraisal of which yields knowledge. What is missing in Heller's model is the distinct role that evidence plays between information and knowledge. Neither in his book [ 19] nor in the underlying paper [ 20] does he define evidence. However, in their 2002 publication, Heller and Page offer a list of statistical and implementation characteristics they see as methods with an appropriate population focus that can be aligned with the methods used in evidence-based medicine because the authors consider the entire process from data via information to knowledge to be evidence-generating.

I should stress that knowledge isn't something out there for us to discover. Instead, knowledge is made. In this commentary, I have outlined a framework that builds on Ackoff's knowledge-hierarchy, in which data give rise to information, which leads to knowledge and finally wisdom. My version of the model drops the notion of wisdom, because it is too imprecise a notion to be useful in a health science context. Instead, I suggest to insert the notion of evidence into the inferential sequence between information and knowledge. Data are used mainly as raw material for information generation. When these data are put into context, they yield information that may be useful as evidence. Based on such evidence, knowledge is generated. Knowledge is evidence-based belief that is predictive, testable, and consistently successful, as judged by consensus among stakeholders. I hope that this proposed modification of Ackoff's framework will contribute to the progress of health informatics and data science.

References

1. Ackoff RL. 1989. From data to wisdom. J Appl Syst Anal. 16, 3-9. [ Google Scholar]

6. Brownson RC, Fielding JE, Maylahn CM. 2009. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health. 30, 175-201. 10.1146/annurev.publhealth.031308.100134 [[ PubMed] [ CrossRef] Google Scholar]

8. Dammann O. 2018. Hill's Heuristics and Explanatory Coherentism in Epidemiology. Am J Epidemiol. 187(1), 1-6. 10.1093/aje/kwx216 [[ PubMed] [ CrossRef] Google Scholar]

14. Green LW, Ottoson JM, Garcia C, Hiatt RA. 2009. Diffusion theory and knowledge dissemination, utilization, and integration in public health. Annu Rev Public Health. 30, 151-74. 10.1146/annurev.publhealth.031308.100049 [[ PubMed] [ CrossRef] Google Scholar]

20. Heller RF, Page J. 2002. A population perspective to evidence based medicine: "evidence for population health. J Epidemiol Community Health. 56(1), 45-47. 10.1136/jech.56.1.45 [[ PubMed] [ CrossRef] PMC free article] [ Google Scholar]

Articles from Online Journal of Public Health Informatics are provided here courtesy of University of Illinois at Chicago Library

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Data, Information, Evidence, and Knowledge:: A Proposal for Health Informatics and Data Science

# data[].attributes.attachment_link.description
A Proposal for Health Informatics and Data Science In this commentary, I revisit and modify Ackoff's data-information-knowledge-wisdom (DIKW) hierarchy. I suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge (DIEK). This framework defines data as raw symbols, which become information when they are contextualized.

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https://www.youtube.com/watch?v=7rDEG3jpxYw

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-->A powerful video on patient safety from 2014/2015. This opened my eyes to how many of the progressions in informatics made over the years since this event have positively impacted patient safety. Many of these advancements in which could have prevented this medical error from occurring. The utilization of an informatics nurse or informatics team most certainly could have prevented this event all together.

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Tulane Center for Advanced Medical Simulation and Team Training

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TeamSTEPPS - Patient Stories: Near Fatal  A Patient Safety Story

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AMIA - American Medical Informatics Association

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What is Informatics?

Biomedical and health informatics applies principles of computer and information science to the advancement of life sciences research, health professions education, public health, and patient care. This multidisciplinary and integrative field focuses on health information technologies (HIT), and involves the computer, cognitive, and social sciences.

Informatics is the science of how to use data, information and knowledge to improve human health and the delivery of health care services. Health IT is part of informatics and an essential aspect of AMIA, but technology and technological considerations are only one component of the association's work. Health IT enables advancements in health care by providing the tools with which to set knowledge in motion. Biomedical and health informatics has developed its own areas of emphasis and approaches that sets it apart from other professions and disciplines Biomedical informatics (BMI) is the interdisciplinary, scientific field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.

BMI develops, studies and applies theories, methods and processes for the generation, storage, retrieval, use, and sharing of biomedical data, information, and knowledge.

BMI builds on computing, communication and information sciences and technologies and their application in biomedicine.

BMI investigates and supports reasoning, modeling, simulation, experimentation and translation across the spectrum from molecules to populations, dealing with a variety of biological systems, bridging basic and clinical research and practice, and the healthcare enterprise.

BMI, recognizing that people are the ultimate users of biomedical information, draws upon the social and behavioral sciences to inform the design and evaluation of technical solutions and the evolution of complex economic, ethical, social, educational, and organizational systems.

The growing role of HIT has created the need to broaden and deepen the pool of workers who are able to help organizations deal effectively with their investment in information technology and, thus, enhance the prospects for major improvements in the safety, quality, effectiveness and efficiency of care. Biomedical and health informaticians understand the workflow of organizations as well as the potential and limitations of information technology. Informaticians conduct research and apply findings to improve processes and propose solutions to technical, clinical, and organizational challenges hampering successful technology implementations.
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Areas of Practice

AMIA supports the following practice areas:

Changing the Way We Approach Health and Healthcare

Through education, training, accreditation and certification, AMIA supports the current and next generation of informatics professionals by:

Providing members opportunities to grow professionally, no matter what their career level or discipline.

Fostering collaboration and networking to support members' work to improve people's lives.

Expanding members' leadership opportunities within the association and in the field.