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Because of the push for autonomy in healthcare through staff and patients, it is important to optimize how we can make information accurate, accessible, and "user-friendly." For example, one of the most critical uses of informatics in healthcare is the implementation of the Electronic Health Record. Many patients can now access this information in real time through a desktop computer or app on their cell phone. To prevent errors and make it easier to use, most entities are using a system that collaborates with other essential healthcare programs and equipment. In the hospital settings we are able to take a patient's blood glucose at the bedside and it will automatically upload to the patient's chart. This bridges the gap for error opportunity and also aids in preventing delays in care so that the information is recorded in realtime. The most important point to takeaway is that health informatics is apart of our everyday life and it is our responsible to continue to work on improving how all of this comes together so that we can be more efficient and have better patient outcomes.

Upload: http://nursing-informatics.weebly.com/advantages-of-ehrs--ehrs-ownership.html

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The main objective with the initiation of TIGER was to promote optimal health universally through access to information through technology. The goal is to eliminate health disparities through the power of information on one's health. TIGER is a global initiative that works directly with government entities to promote health reform and policies. One example of a program that the TIGER initiative is working on is the delivery of medical supplies via drones in India.

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Standardizing the way we take about health is vital to improving our healthcare system. This allows for accurate diagnosing, orders, and even coding and billing insurances. The EHR can help in this process because it allows people inside and outside of the hospital to have the same understanding and access to information to improve the quality of care that we deliver as well as patient outcomes. I'm sure many of us have experiences where we are fumbling through a paper chart or copies that a facility sent over on a transfer of a sick patient. It takes hours to organize and collect information. A universal EHR can decrease time wasting on flipping through papers, already organize where to find specific information and data such as lab results, radiology reports, and vital signs. Because our programs "talk to each other" we can eliminate the risk for error and retrieve our information quickly to attend to our patient sooner.

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The relationship between data, information, knowledge, and wisdom was discussed. The main takeaway explained how a piece of data only has meaning when it has background (or information) to provide context and meaning to what that data point means. After you gathered information, knowledge creates additional understanding with programs that provide algorithms with computers. For example. our charting system in the hospital will flag a patient at risk for sepsis when data is input such as blood pressure, temperature, labs, etc. The wisdom portion of technologies include the alerts it will provide to the medical staff to identify these patients and continue to monitor specific indicators that the computer has been programmed to notice.
Upload: https://ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/Columns/Informatics/Informatics-Evolution-of-Nelson-Model-Part-2.html

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https://www.youtube.com/watch?v=pzS--PaGC9o

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http://ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/Columns/Informatics/TheTigerProject.html

https://www.healthcareitnews.com/news/asia/indian-council-medical-research-releases-guidance-using-drones-medical-supply-delivery

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The Indian Council of Medical Research, India's peak body for biomedical research, has published its guidance for using drone technology in healthcare.
WHAT IT'S FOR
The 28-page document presents step-by-step guidance on obtaining regulatory approvals for using various kinds of drones and utilising air space for delivering medical supplies. It also discusses how to select suitable drones for medical supplies and provides criteria for choosing take-off and landing points for drones. The guidance also identifies the types of medical supplies that can be delivered via drone; the preparation of carrier boxes and the process of loading and unloading items from drones; and quality check aspects.

Additionally, it stresses the requirement and training needs of healthcare workers for drone-based delivery.

Based on the document, medical items approved for drone deliveries include vaccines and temperature-sensitive medical supplies with preservation needs of between 2-8 degrees Celsius; non-temperature sensitive supplies such as tablets, syrups, and surgicals; blood bags, and diagnostic biological samples such as tissues, urine, blood, pus, sputum, saliva or frozen specimens.
WHY IT MATTERS
The ICMR noted the "immense potential" of drone technology in healthcare as it can serve as a last-mile logistics solution for delivering medical supplies in hard-to-reach areas. While there is growing interest, there is not enough information out there on conducting drone deliveries, and this is where its latest guidance can fill the gap in knowledge of using drones in healthcare transport.
THE LARGER TREND
The guidance was based on ICMR's pilot project for assessing the feasibility of using drones for delivering COVID-19 vaccines and medical supplies in the northeastern states of Manipur and Nagaland in October last year. Samiran Panda, head of Epidemiology and Communicable Diseases at ICMR, said the country's COVID-19 vaccination drive gained a "renewed vigour" following the project. "In [the] future, drones are expected to play wider roles by offering a time-efficient mode for delivering lifesaving medications to far-flung and remote locations in India," he said.

The first medical drone delivery in India happened in September 2021 after the government liberalised its drone policy that year. The Medicine from the Sky project was done in the southern state of Telangana by the World Economic Forum, together with Apollo Hospital's HealthNet Global and government think tank NITI Aayog.

Early this year, Retailio, a local digital pharmaceutical marketplace, tried out drone medicine delivery in partnership with drone logistics firm Redwing Labs. The programme was conducted in the Chickkaballapur district in the southwestern state of Karnataka.
ON THE RECORD
"Since the usage of drones in the healthcare system is a relatively new venture in the country, there are possibilities of confusion and lack of clarity on standard operating procedures. This guidance document, in compliance with the New Drone Rules-2021, presents detailed requirements to the drone operators for obtaining regulatory approvals; selecting suitable specification[s] of drones and [their] accessories for conducting specialised delivery operations; identifying and training of human resource for efficient on-ground deployment; and handling unforeseen events during field operations," Dr Balram Bhargava, director-general of ICMR, commented.

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Indian Council of Medical Research releases guidance on using drones for medical supply delivery

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The Indian Council of Medical Research, India's peak body for biomedical research, has published its guidance for using drone technology in healthcare. WHAT IT'S FOR The 28-page document presents step-by-step guidance on obtaining regulatory approvals for using various kinds of drones and utilising air space for delivering medical supplies.

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Week 3: Interoperability and how it affects our healthcare system

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Week 3: Interoperability and how it affects our healthcare system

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

Everything is so accessible now-a-days through smart devices such as mobile phones, smart watches, security systems, or cars. Information is sent from multiple devices or programs and controlled by one central space such as an app.

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0:00 / 4:18•Watch full video

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What is the Internet of Things and Why Should You Care ?

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How can we reach the older generation in promoting health informatics and autonomy? Easy access to healthcare via smart phones or even desktop computers can allow people to make appointments with their healthcare provider, review medication, order new prescriptions, or even review test results. There is a gap in aiding the older generation in experiencing these benefits and improving their health. It is vital for us to promote autonomy and advocate for ways to better their accessibility to health.

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Could the HIMSS be better linked with the World Health Organization? Education and information is power in healthcare and people taking control of their our health. I believe it would be beneficial for these two entities to join forcing in promoting worldwide improvements to the broken healthcare system and helping to decrease health disparities.

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In our attempts to create a central system that collects and provides the same level of terminology, many facilities continue to use different platforms for EHRs. If the same program was used nationally it will eliminate many barriers and errors in our healthcare system. Could the government require one specific system to improve this large problem? Or should the government stay hands off in this process?

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Was healthcare more secure prior to implementing a universal program that collects all of your health data? What about hackers? How can we protect our patients? What are the disadvantages?
A central information program can also provide many advantages and prevent HIPAA violation by having a secure platform to send information or upload data to.

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This article discusses how informaticists are working to bridge gaps in healthcare and prevents disparities and improve health equity with the implementation of informatics.

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Developing and maintaining a standardized vocabulary in healthcare can severely improve patient safety, coding diagnoses, and promoting accuracy in chart system.

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This article touches on the challenges that I discussed in the muddy waters sections. They identified patient safety and positive user interfaces as main objectives in implementing new EHR systems.

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This week's discussion talks about how implementing EHRs can reduce errors in the healthcare system. In order to build an EHR there needed to be proof of secure, technological benefits that could be used to improve the healthcare system. Stage 1 (2011-2012) worked to capture and share data. Stage 2 (2014) worked to advance clinical processes such as warnings and alerts in the medical record, electronic prescriptions, and secure patient health information. Stage 3

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"Meaningful use" is described in several ways but is more clearly defined in the Final Rule listed in this article. 

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There appears to be a big concern for how laboratories are able to use the EHR requirements successfully. These include red flags with abnormal results, setting a reference range for the program, a program used to educate with abnormal results, and ensure accurate reporting of results to the physician and the patient. There are a lot of discrepancies with orders from different providers that can be a cause for error.

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Nurses’ Attitudes Toward Meaningful Use Technologies: An Int... : CIN: Computers, Informatics, Nursing (lww.com)

It is essential to work with staff to ensure satisfaction when implementing change in practice. Introducing a change project as large as an EHR can be challenging. Working with nursing to promote user-friendly education, receive feedback to improve the program, and work together to promote the change is vital to success.

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The video that impacted me this week about the importance of the electronic health records was called, "Electronic Health Records (EHRs) Can Save Lives- Regina Holiday's Story." The woman in the video talks about how difficult it was to get a copy of her husband's medical history and how it could have improved his care. Just this instance is overwhelming evidence that having an EHR is vital to the healthcare system and autonomy for patients and families. Old practice healthcare followed more along the lines of "the Doctor told me to do this but I don't know why." Today's healthcare works well through teamwork with our patients, autonomy, and advocacy.

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This website discusses the advantages of having an electronic medical record. They talked about the importance of decrease communication errors, errors through difficulty reading other's handwriting, improving workloads, and having security of health records.

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When looking at the Clinical Decision Support (CDS) tools, one concern that comes to mind is staff relying on the system to flag issues with information that's input incorrectly, alerts to order duplication, or medication/allergy errors. More recently a criminal case took place involving a bedside nurse who give a patient vecuronium (a paralytic) instead of the intended versed to calm her nerves before a scan. The nurse was in a hurry and cleared all of the several alerts that the CDS created when overriding this medication. An example includes "paralytic, only use when patient has an artificial airway." In healthcare we are seeing alarm fatigue and exposure to alert messages so frequently that staff is choosing to ignore or cancel the alert.

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An overview of clinical decision support systems: benefits, risks, and strategies for success - PMC (nih.gov)

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Introduction: What is a clinical decision support system?

A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. A traditional CDSS is comprised of software designed to be a direct aid to clinical-decision making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision. CDSSs today are primarily used at the point-of-care, for the clinician to combine their knowledge with information or suggestions provided by the CDSS. Increasingly however, there are CDSS being developed with the capability to leverage data and observations otherwise unobtainable or uninterpretable by humans.

Computer-based CDSSs can be traced to the 1970s. At the time, they had poor system integration, were time intensive and often limited to academic pursuits. There were also ethical and legal issues raised around the use of computers in medicine, physician autonomy, and who would be at fault when using the recommendation of a system with imperfect 'explainability'. Presently, CDSS often make use of web-applications or integration with electronic health records (EHR) and computerized provider order entry (CPOE) systems. They can be administered through desktop, tablet, smartphone, but also other devices such as biometric monitoring and wearable health technology. These devices may or may not produce outputs directly on the device or be linked into EHR databases.

CDSSs have been classified and subdivided into various categories and types, including intervention timing, and whether they have active or passive delivery. CDSS are frequently classified as knowledge-based or non-knowledge based. In knowledge-based systems, rules (IF-THEN statements) are created, with the system retrieving data to evaluate the rule, and producing an action or output; Rules can be made using literature-based, practice-based, or patient-directed evidence. CDSS that are non-knowledge based still require a data source, but the decision leverages artificial intelligence (AI), machine learning (ML), or statistical pattern recognition, rather than being programmed to follow expert medical knowledge. Non-knowledge based CDSS, although a rapidly growing use case for AI in medicine, are rife with challenges including problems understanding the logic that AI uses to produce recommendations (black boxes), and problems with data availability. They have yet to reach widespread implementation. Both types of CDSS have common components with subtle differences, illustrated in Fig. .

CDSS have been endorsed by the US Government's Health and Medicare acts, financially incentivizing CDS implementation into EHRs. In 2013, an estimated 41% of U.S. hospitals with an EHR, also had a CDSS, and in 2017, 40.2% of US hospitals had advanced CDS capability (HIMSS Stage 6). Elsewhere, adoption rates of EMRs have been promising, with approximately 62% of practitioners in Canada in 2013. Canada has had significant endorsement from the government level, as well as Infoway, a not-for-profit corporation. England has also been a world leader in healthcare IT investment, with up to 20 billion euros invested back in 2010. Several countries have also managed to implement national health records, at least for patient-facing data, including Denmark, Estonia, Australia, and others.

The scope of functions provided by CDSS is vast, including diagnostics, alarm systems, disease management, prescription (Rx), drug control, and much more. They can manifest as computerized alerts and reminders, computerized guidelines, order sets, patient data reports, documentation templates, and clinical workflow tools. Each CDSS function will be discussed in detail throughout this review, with the potential and realized benefits of these functions, as well as unintended negative consequences, and strategies to avoid harm from CDSS. Methodology used to inform the review is shown in Box 1.

Box 1. Methods and sources used for this overview

MEDLINE search 1980-January 2018. Key words: CDSS, diagnostic decision support system/DDSS, personal health record/PHR decision support, EHR decision support

Hand searches of the references of retrieved literature

University libraries searching for texts on clinical decision support systems and other keywords mentioned above

Personal and local experience working with healthcare technology and decision support systems

Functions and advantages of CDSS

Patient safety

Strategies to reduce medication errors commonly make use of CDSS (Table ). Errors involving drug-drug interactions (DDI) are cited as common and preventable, with up to 65% of inpatients being exposed to one or more potentially harmful combinations. CPOE systems are now designed with drug safety software that has safeguards for dosing, duplication of therapies, and DDI checking. The types of alerts generated by these systems are among the most disseminated kind of decision support. However, studies have found a high level of variability between how alerts for DDIs are displayed (e.g., passive or active/disruptive), which are prioritized, and in the algorithms used to identify DDIs. Systems often have varying degrees of irrelevant alerts presented, and there is no standard for how best to implement which alerts to providers. The US Office of the National Coordinator for Health Information Technology has developed a list of 'high-priority' list of DDIs for CDS, which has reached various levels of endorsement and deployment in CDSS' of other countries including the U.K., Belgium, and Korea.

Table 1

1. Functions and advantages of CDSS

2A. Potential harm of CDSS

2B. Solution(s) to mitigate harm

2C. Explanation of solution(s)

Prioritize critical alerts, minimize use of disruptive alerts for non-critical indications.

Alert fatigue might be thwarted by prioritizing and selecting alerts that are critically important, that will have the greatest impact, and by tailoring alerts to specific specialties and severities (personalization).

DDI testing software should ideally be programmed with an algorithm that incorporates concomitant medication, lab values, patient demographics, and administration times, to be as specific as possible.

Avoid prescriptiveness in system design. Evaluate system impact on an ongoing basis.

Systems should be set up to be useful to clinicians, without jeopardizing autonomy or being too 'prescriptive' and definitive. It is important to conduct analysis to see how the system is being used in the long term, after implementation. If accuracy is an issue, design changes might need to be taken to prompt extra checks or confirmation of orders.

Design and plan for longitudinal cost analysis at the outset. Specify measurements for non-financial benefits where possible.

An analysis should be done to determine if the costs are justified and if there is a good return on investment. Cost analysis is notoriously missing in the literature, but examples can be found. Payers may be more willing to support CDSS if cost-savings can be shown elsewhere in the system / process. This means looking at more than just direct costs a using metrics such as patient outcomes or quality-adjusted life years (QALY).

(1) Knowledge Management (KM) Service in place, with a focus on translation to CDSS systems.

(1) Facilitates scheduled review, methods for acquiring and implementing new knowledge, and streamlined processes for gathering physician feedback on the system as well as training users on why certain data entry and standardization of data entry practices. Standards for organizing KM management have been published.

(2) It is important to identify changes in performance and use over time. In addition, the quality of the data repository should be monitored and it is also important to ensure that conclusions are not being made on corrupted or poor quality data beforehand.

(2) System for measurement and analysis of CDSS performance.

Reference expert knowledge-include scientific references in messages where appropriate.

To provide a verifiable source of information to the user on why the recommendation exists. In addition to increasing trust, this may provide direction for users to update their knowledge in case they were not aware of the recommendation.

Many systems also query reasons for not following a recommendation in order to elucidate the source of mistrust. This is a good idea, but should not be mandatory or 'bulky' in design.

(1) Adoption of industry standards.

Major open standards for structural and semantic interoperability and exchange continue to be developed and improved by organizations such as Health Level 7 International (HL7), SNOMED International, Digital Imaging and Communications (DICOM) for imaging standards, and many others. As much as possible, these standards should be adopted at all levels within the healthcare organization, and with the external systems being used.

Cloud-based EHR architecture allows for more open architecture, and flexible connectivity between systems. As with any medical system, security must be assured through compliance with legislation such as Health Insurance Portability and Accountability Act (HIPAA) in the USA, Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada, and the Data Protection Directive and General Data Protection Regulation (GDPR) in Europe. In the future, we may also see blockchain used to enable greater interoperability and improve security for health information exchange (HIE).

(2) Secure cloud services and blockchain.

(1) Conform to existing functionality.

(1) Maintaining consistency with the user interface of the pre-existing system (if there is one) is crucial to ensure users don't have a steep learning curve to use the system.

(2) Adequate training should be available and easily accessible for users. Training should ideally be done in person by a clinician leader with vast EHR experience to generate buy-in. Training needs to be available on an ongoing basis, as new staff and users join. One strategy is to have on-site team members designated as elite users, and capable of providing training sessions.

(2) Adequate training made available at launch.

Better Documentation
(1) Expert Knowledge of interlinked systems.

The team needs to be familiar and have expert knowledge of all external systems that feed data into the database used by the CDSS.

Experts recommend testing clinical rules for PPV and NPV during the process of development and implementation. If user generated data is an issue, it may be that physicians have not received the proper training on how to read, interpret and respond to alerts, or are depending on pharmacists to check medication orders before dispensation.

(2) IT testing/debugging during development and implementation stage.

(1) Usability evaluation.

(1) Rigorous and iterative usability evaluations and pilot testing should be conducted on CDSS before using them in clinical settings. Many usability assessment tools are available, along with other quantitative methods and frameworks.

(2) Unless a goal of the CDSS is to change the care process, the CDSS should be designed to fit within or conform to the current user workflows.

(2) Workflow modeling.

Other systems targeting patient safety include electronic drug dispensing systems (EDDS), and bar-code point-of-care (BPOC) medication administration systems. These are often implemented together to create a 'closed loop', where each step of the process (prescribing, transcribing, dispensing, administering) is computerized and occurs within a connected system. At administration, the medication is automatically identified through radio-frequency identification (RFID) or barcodes and crosschecked with patient information and prescriptions. This presents another target for CDSS and the potential benefit is the prevention of medication administration errors occurring at the 'bedside' (opposed to further upstream). Adoption is relatively low, partly due to high technology requirements and costs. However; studies show good efficacy for these systems in reducing errors. Mohoney et al. showed that many of these systems can be combined with CPOE and CDSS simultaneously, with reduced prescribing error rates for drug allergy detection, excessive dosing, and incomplete or unclear ordering. As with most CDSS, errors can still be made if providers omit or deliberately work around the technology.

CDSS also improve patient safety through reminder systems for other medical events, and not just those that are medication related. Among numerous examples, a CDSS for blood glucose measurement i n the ICU was able to decrease the number of hypoglycemia events. This CDSS automatically prompted nurses to take a glucose measurement according to a local glucose monitoring protocol, which specified how often measurements should be done according to specific patient demographics and previous glucose levels/trends.

Overall, CDSS targeting patient safety through CPOE and other systems have generally been successful in reducing prescribing and dosing errors, contraindications through automated warnings, drug-event monitoring and more. Patient safety can be considered a secondary objective (or requirement) of almost all types of CDSS, no matter the primary purpose for their implementation.

Clinical management

Studies have shown CDSS can increase adherence to clinical guidelines. This is significant because traditional clinical guidelines and care pathways have been shown to be difficult to implement in practice with low clinician adherance. The assumption that practitioners will read, internalize, and implement new guidelines has not held true. However, the rules implicitly encoded in guidelines can be literally encoded into CDSS. Such CDSS can take a variety of forms, from standardized order sets for a targeted case, alerts to a specific protocol for the patients it pertains to, reminders for testing, etc. Furthermore, CDSS can assist with managing patients on research/treatment protocols, tracking and placing orders, follow-up for referrals, as well as ensuring preventative care.

CDSS can also alert clinicians to reach out to patients who have not followed management plans, or are due for follow-up, and help identify patients eligible for research based on specific criteria. A CDSS designed and implemented at Cleveland Clinic provides a point-of-care alert to physicians when a patient's record matches clinical trial criteria. The alert prompts the user to complete a form which establishes eligibility and consent-to-contact, forwards the patient's chart to the study coordinator, and prints a clinical trial patient information sheet.

Cost containment

CDSS can be cost-effective for health systems, through clinical interventions, decreasing inpatient length-of-stay, CPOE-integrated systems suggesting cheaper medication alternatives, or reducing test duplication. A CPOE-rule was implemented in a pediatric cardiovascular intensive care unit (ICU) that limited the scheduling of blood count, chemistry and coagulation panels to a 24-h interval. This reduced laboratory resource utilization with a projected cost savings of $717,538 per year, without increasing length of stay (LOS), or mortality.

CDSS can notify the user of cheaper alternatives to drugs, or conditions that insurance companies will cover. In Germany, many inpatients are switched to drugs on hospital drug formularies. After finding that 1 in 5 substitutions were incorrect, Heidelberg hospital developed a drug-switch algorithm and integrated it into their existing CPOE system. The CDSS could switch 91.6% of 202 medication consultations automatically, with no errors, increasing safety, reducing workload and reducing cost for providers.

Administrative functions

CDSS provide support for clinical and diagnostic coding, ordering of procedures and tests, and patient triage. Designed algorithms can suggest a refined list of diagnostics codes to aid physicians in selecting the most suitable one(s). A CDSS was conceived to address inaccuracy of ICD-9 emergency department(ED) admission coding (ICD is International Statistical Classification of Diseases, standardized codes used to represent diseases and diagnoses). The tool used an anatomographical interface (visual, interactive representation of the human body) linked to ICD codes to help ED physicians accurately find diagnostic admission codes faster.

CDSS can directly improve quality of clinical documentation. An obstetric CDSS featured an enhanced prompting system, significantly improving documentation of indications for labor induction and estimated fetal weight, compared to control hospital. Documentation accuracy is important because it can directly aid clinical protocols. For example, a CDSS was implemented to ensure patients were properly vaccinated following splenectomy, to combat the increased risk of infections (including pneumococcal, Haemophilus influenzae, meningococcal, etc.) that comes with spleen removal. However, the authors found that 71% of patients with the term 'splenectomy' in their EHR did not have it documented on their problem list (which was what triggers the CDSS alert). A supplemental CDSS was then developed to enhance problem list documentation of splenectomy, and improve the utility of the original vaccination CDSS.

Diagnostics support

CDSS for clinical diagnosis are known as diagnostic decision support systems (DDSS). These systems have traditionally provided a computerized 'consultation' or filtering step, whereby they might be provided data/user selections, and then output a list of possible or probable diagnoses. Unfortunately, DDSS have not had as much influence as other types of CDSS (yet) for reasons including negative physician perceptions and biases, poor accuracy (often due to gaps in data availability), and poor system integration requiring manual data entry. The latter is improving with better EHR-integration and standardized vocabulary like Snomed Clinical Terms.

A good example of an effective DDSS is one which was created by Kunhimangalam et al. for diagnosis of peripheral neuropathy using fuzzy logic. Through 24 input fields which include symptoms and diagnostic test outputs, they achieved 93% accuracy compared to experts at identifying motor, sensory, mixed neuropathies, or normal cases. While this has great utility, especially in countries with less access to established clinical experts, there is also a desire for systems that can supplement specialist diagnostics. DXplain is an electronic reference based DDSS that provides probable diagnosis based on clinical manifestations. In a randomized control trial involving 87 family medicine residents, those randomized to use the system showed significantly higher accuracy (84% vs. 74%) on a validated diagnosis test involving 30 clinical cases.

Given the known incidence of diagnostic errors, particularly in primary care, there is a lot of hope for CDSS and IT solutions to bring improvements to diagnosis. We are now seeing diagnostic systems being developed with non-knowledge-based techniques like machine learning, which may pave the way for more accurate diagnosis. The Babylon AI powered Triage and Diagnostic System in the U.K. is a good example of the potential, but also of the work that still has to be done before these systems are ready for primetime.

Diagnostics support: imaging

Knowledge-based imaging CDSS are typically used for image ordering, where CDSS can aid radiologists in selecting the most appropriate test to run, providing reminders of best practice guidelines, or alerting contraindications to contrast, for example. An interventional CDS for image ordering at Virginia Mason Medical Center was shown to substantially decrease the utilization rate of lumbar MRI for low back pain, head MRI for headache, and sinus CT for sinusitis. The CDS required a series of questions to be answered by providers prior to image ordering (POC), to verify appropriateness. Importantly, if an image was denied, an alternative was suggested by the system. Another commercialized example is RadWise®, which guides clinicians to the most relevant imaging order by analyzing patient symptoms and matching them with a large database of diagnoses, while also providing appropriate use recommendations at the point of care.

There is great interest in non-knowledge based CDS for enhanced imaging and precision radiology ('radiomics'). With images accounting for increasing amounts of medical data, but requiring extensive manual interpretation, providers need technologies to aid them in extracting, visualizing, and interpreting. AI technologies are proving capable of providing insights into data beyond what humans can. To do so, these technologies make use of advanced pixel recognition and image classification algorithms, most prominently: deep learning (DL). IBM Watson Health, DeepMind, Google, and other companies are at the forefront, developing products for use in tumor detection, medical imaging interpretation, diabetic retinopathy diagnosis, Alzheimer's diagnosis through multimodal feature learning, and countless more. IBM Watson's 'Eyes of Watson', has been able to combine image recognition of a brain scan with text recognition of case descriptions to provide comprehensive decision support (or what IBM describes as a 'cognitive assistant').

Several projects have been able to demonstrate performance that is disputably 'on par' with human experts. For example, Google's team trained a deep convolutional neural network (CNN) to detect diabetic retinopathy (blood vessel damage in the eye) from a dataset of 130,000 retinal images with a very high sensitivity and specificity. The algorithms performance was on par with US board certified ophthalmologists. Another study just recently published by the Stanford group demonstrated a CNN for detecting arrhythmias on electrocardiogram that exceeded the accuracy (F1 and sensitivity with matched specificity) of the average cardiologist on all rhythm classes. With the current rate of progress, some experts controversially speculate that in 15-20 years, the majority of diagnostic imaging interpretation will be done (or at least pre-processed) by computers. For the time being however, we should think of these early systems as an addition or augmentation to a clinician's available toolset.

Diagnostics support: laboratory and pathology

Another subset of diagnostics where CDSS can be useful is laboratory testing and interpretation. Alerts and reminders for abnormal lab results are simple and ubiquitous in EHR systems. CDSS can also extend the utility of lab-based tests for the purpose of avoiding riskier or more invasive diagnostics. In Hepatitis B and C testing, liver biopsies are considered the gold standard for diagnosis, while non-invasive lab tests are not accurate enough to be accepted. However; AI models are being developed that combine multiple tests (serum markers, imaging, and gene tests) to produce much greater accuracy. There is also application for CDSS as an interpretation tool where a test's reference ranges are highly personalized, for example age, sex, or disease subtypes.

Pathology reports are crucial as decision points for many other medical specialties. Some CDSS can be used for automated tumor grading. This was done for urinary bladder tumor grading and estimating recurrence, with up to 93% accuracy. The same has been done for brain tumor classification and grading. There are many other examples including computerized ECG analysis, automated arterial blood gas interpretation, protein electrophoresis reports, and CDSS for blood cell counting.

Patient-facing decision support

With the advent of the 'Personal Health Record' (PHR), we are seeing CDS functionality integrated, similar to EHRs, with the patient as the end user or 'manager' of the data. This is a great step towards patient-focused care, and CDS-supported PHRs are the ideal tool to implement shared decision-making between patient and provider, specifically because CDSS can remove a 'lack of information' as a barrier to a patient's participation in their own care. PHRs are frequently designed as an extension of commercial EHR software, or as standalone web-based or mobile-based applications. When connected to EHRs, PHRs can have a two way relationship, whereby information entered directly by the patient can be available to their providers, and also information in the EHR can be transmitted to the PHR for patients to view.

One of the earliest PHRs, the "Patient Gateway", was simply a dashboard for patients to view medications and labs, and communicate with their physicians. This has expanded and some systems now allow patients to modify their own record of care, effecting the EHR data as well. Another example is Vanderbilt University's MyHealthAtVanderbilt, a PHR fully integrated into the institutional EHR. In addition to disease-targeted delivery of patient educational materials, they incorporated a Flu Tool for patients with flu-like symptoms to decide the level of care they need and then help them seek treatment. Symptom tracking is a useful and common feature of PHRs, but the variety of collected data is virtually limitless, from allergies to insurance coverage to prescription and medication information. Furthermore, PHRs and other patient monitoring applications can be designed to collect information from health devices and other wearables, to create actionable insights for providers. An excellent example exists in diabetes care. Many systems are already in use, but one in particular pioneered by the Stanford School of Medicine uses a wearable glucose monitor which transmits data to an Apple device (HealthKit). Apple has made HealthKit interoperable with the Epic EHR and Epic PHR, "MyChart". This successfully allows providers to monitor glucose trends in their patients in between visits, and contact them through MyChart for follow up or urgent recommendations. The pilot study demonstrated improved provider workflow, communication with patients, and ultimately quality of care. Various other medical fields are deploying similar systems for monitoring that combines PHR/EHR, wearable technologies, and CDSS, including but not limited to heart failure (cardiology), hypertension, sleep apnea, palliative/elder care, and more.

It is worth noting that as PHRs have become more advanced with CDSS capabilities, there has also been increasing emphasis on the design of these systems to serve shared decision making between patient and provider, and to be interactive tools to make patients more knowledgeable/involved in their own care. PHRs that only serve as a repository for health information are now seen as missing the mark, particularly by patients themselves.

Pitfalls of CDSS

Fragmented workflows

CDSS can disrupt clinician workflow, especially in the case of stand-alone systems. Many early CDSS were designed as systems that required the provider to document or source information outside their typical workspace. CDSS also disrupt workflow if designed without human information processing and behaviors in mind. In response, CDSS have been designed using the 'think-aloud' method to model practitioners' workflow and create a system with better usability.

Disrupted workflow can lead to increased cognitive effort, more time required to complete tasks, and less time face-to-face with patients. Even when CDSS are well integrated within existing information systems, there can be disconnect between face-to-face interactions and interaction with a computer workstation. Studies have found that practitioners with more experiential knowledge are less likely to use, and more likely to override CDSS.

Alert fatigue and inappropriate alerts

Studies have found up to 95% of CDSS alerts are inconsequential, and often times physicians disagree with or distrust alerts. Other times they just do not read them. If physicians are presented with excessive/unimportant alerts, they can suffer from alert fatigue.

Disruptive alerts should be limited to more life-threatening or consequential contraindications, such as serious allergies. However; even allergy alerts can be incorrect, and clinicians will often verify themselves, especially if the source is another site/hospital/practitioner. Medication alerts can also be specialty specific, but irrelevant when taken out of context. For example, an alert against using broad-spectrum antibiotics such as vancomycin may be inappropriate in ICU. An alert against duplicate medications may be inappropriate in inflammatory bowel disease clinics, where the same class of drug can be applied through different administration routes for increased effect.

Impact on user skill

Prior to CPOE and CDSS, healthcare providers, pharmacists, and nurses were relied upon exclusively to double-check orders. CDSS can create the impression that verifying the accuracy of an order is unnecessary or automatic. This is an important myth to dispel.

It is also important to consider the potential long-term effect of a CDSS on users. Over time a CDSS can exert a training effect, so that the CDSS itself may no longer be required. Coined the "carry-over effect", it is most likely with CDSS that are educational in nature. Conversely, providers may develop too much reliance or trust on a CDSS for a specific task. This could be compared to using a calculator for mathematical operations over a long period of time, and then having poorer mental math skills. It is potentially problematic as the user has less independence and will be less equipped for that task should they switch to an environment without the CDSS.

CDSS may be dependent on computer literacy

Lack of technological proficiency can be hindering when engaging with a CDSS. This can vary by the design details of the CDSS, but some have been found to be overly complex, relying too much on user skill. Systems should aim to stay as close to the core functionality of the pre-existing system as possible. Regardless, all new systems have a learning period, and so baseline evaluations of users' technological competence may be appropriate. Further training can then be provided to facilitate full use of CDSS capabilities, or more explicit guidance incorporated into the CDSS' recommendations themselves. This information could be implemented as info buttons to be non-disruptive.

System and content maintenance

Maintenance of CDSS is an important but often neglected part of the CDSS life-cycle. This includes technical maintenance of systems, applications and databases that power the CDSS. Another challenge is the maintenance of knowledge-base and its rules, which must keep apace with the fast-changing nature of medical practice and clinical guidelines. Even the most advanced healthcare institutions report difficulty keeping their systems up to date as knowledge inevitably changes. Order sets and the algorithmic rules behind the CDSS have been identified as particularly difficult.

Operational impact of poor data quality and incorrect content

EHRs and CDSSs rely on data from external, dynamic systems and this can create novel deficiencies. As an example, some CDSS modules might encourage ordering even when the hospital lacks adequate supplies. In a study by Ash et al., a number of experts indicated that at their hospital, Hemoccult tests or pneumococcal vaccine inventories run out quickly, but this is not communicated to the CDSS.

Medication and problem lists can be problematic, if not updated or used appropriately. At one site, the medication list might be a list of dispensations, which means patients may or may not be taking them(and thus must still be asked in person). Other medication lists are generated from CPOE orders only, thus still requiring manual confirmation that patients are taking the medication. Systems that make it easy to distinguish these are ideal. It is also a major area where PHRs could create a solution, by collecting medication adherence data directly from patients.

In poorly designed systems, users may develop workarounds that compromise data, such as entering generic or incorrect data. The knowledge base of CDSS is dependent on a centralized, large clinical data repository. Quality of data can affect quality of decision support. If data collection or input into the system is unstandardized, the data is effectively corrupted. You may design a system for use at the point-of-care, but when applied to real world environments and data, will not be utilized properly. The importance of using informational standards such as ICD, SNOMED, and others, cannot be understated.

Lack of transportability and interoperability

Despite ongoing development for the better part of three decades, CDSS (and even EHRs in general) suffer from interoperability issues. Many CDSS exist as cumbersome stand-alone systems, or exist in a system that cannot communicate effectively with other systems.

What makes transportability so difficult to achieve? Beyond programming complexities that can make integration difficult, the diversity of clinical data sources is a challenge. There is a reluctance or perceived risk associated with transporting sensitive patient information. Positively, interoperability standards are continuously being developed and improved, such as Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR). These are already being utilized in commercial EHR vendors. Several government agencies, medical organizations and informatics bodies are actively supporting and some even mandating the use of these interoperability standards in health systems.

The cloud also offers a potential solution to interoperability (and other EHR ailments such as data sync, software updating, etc.). Cloud EHRs have open architecture, newer standards, and more flexible connectivity to other systems. It is also a common misconception that data stored on a cloud is more vulnerable. This is not necessarily true. Web-based EHRs are required to store data in high-level storage centers with advanced encryption and other safeguards. They must comply with national data security standards including the Health Insurance Portability and Accountability Act (HIPAA) in the USA, Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada, or the Data Protection Directive and General Data Protection Regulation (GDPR) in Europe, to name a few. They can be just as safe (or just as vulnerable) as traditional, server-based architecture. In fact, there are often fewer people who have access to unencrypted data in cloud storage centers vs. server-based records.

Financial challenges

Up to 74% of those with a CDSS said that financial viability remains a struggle. Outset costs to set up and integrate new systems can be substantial. Ongoing costs can continue to be an issue indefinitely as new staff need to be trained to use the system, and system updates are required to keep pace with current knowledge.

Results from cost analyses of CDSS implementations are mixed, controversial, and sparse. Whether an intervention is cost-effective depends on a wide range of factors, including those specific to the environment, both political and technological. Cost benefit assessment in itself can be limited, with challenges such as a lack of standardized metrics. This is an emerging research area and much work needs to be done to advance our understanding of the financial effects of CDSS.

Conclusion

CDSS have been shown to augment healthcare providers in a variety of decisions and patient care tasks, and today they actively and ubiquitously support delivery of quality care. Some applications of CDSS have more evidence behind them, especially those based on CPOE. Support for CDSS continues to mount in the age of the electronic medical record, and there are still more advances to be made including interoperability, speed and ease of deployment, and affordability. At the same time, we must stay vigilant for potential downfalls of CDSS, which range from simply not working and wasting resources, to fatiguing providers and compromising quality of patient care. Extra precautions and conscientious design must be taken when building, implementing, and maintaining CDSS. A portion of these considerations were covered in this review, but further review will be required in practice, especially as CDSS continue to evolve in complexity through advances in AI, interoperability, and new sources of data.

# data[].attributes.attachment_link.title
An overview of clinical decision support systems: benefits, risks, and strategies for success

# data[].attributes.attachment_link.description
A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. A traditional CDSS is comprised of software designed to be a direct aid to clinical-decision making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision.

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Publishing datasets in healthcare is vital to the future of research and disease patterns. There are many benefits to using this data to identify key points in those at risk for disease including demographics, age, and medical history. It can also be used to track interventions and treatment that resulted in a positive outcome in a patient's health. Using the electronic health record allows computer systems to rapidly progress our healthcare system and help push our research to discover need treatments at a fast pace.

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Looking at datasets can be difficult to navigate as an outside perspective because these are used to support and benefit the researcher and appear to be highly biased. For example, Medicare is focused toward financial aspects of patient care and less about other important data that can be used to benefit research.
https://resdac.org/cms-data/files/mds-30

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What does this file include? (variable highlights)

Resource Utilization Group (RUG) code

Clinical status measures

Physical functioning assessment

Psychological status measures

Psycho-social functioning measure

End-of-life care decisions

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Long Term Care Minimum Data Set (MDS) 3.0

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What does this file include? (variable highlights) Resource Utilization Group (RUG) code Clinical status measures Physical functioning assessment Psychological status measures Psycho-social functioning measure End-of-life care decisions

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https://guides.lib.uw.edu/hsl/data/findclin

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Library Guides: Data Resources in the Health Sciences: Clinical Data

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245488/

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Journal List

BMC Med Genomics

v.11(Suppl 5); 2018

PMC6245488

BMC Med Genomics. 2018; 11(Suppl 5): 102.

Abstract

Background

The right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses.

Methods

The DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search.

Results

The DIR prototype is composed of four major components—dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly.

Conclusions

We have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.

Keywords: Health informatics, Dataset information resource, Knowledge representation, Semantic web, Knowledge extraction

Background

Healthcare data is rapidly growing in the era of big data. An increasing number of researchers are leveraging these datasets to improve the quality of patient care. However, challenges caused by a variety of purposes, designs, and techniques when health data were originally collected boost the complexity and diversity of healthcare datasets. For health data analysis, it requires significant time, energy, and fundamental knowledge to identify, understand, and choose the right datasets. The challenges for students and researchers who have little experience are even more pronounced. A number of online data resources, such as HealthData.gov [1], Data.CDC.gov [2], and Society of General Internal Medicine (SGIM) Research Dataset Compendium [3], integrate basic information for public datasets, which help new investigators choose datasets to a certain extent. However, the simple descriptions in these portals are hardly adequate for them to identify a suitable dataset to delve into. Simple search functions, such as a keywords search, provided by most of the resources cannot handle more complex and less concrete questions that typical novices have, such as finding existing analytical methods that are suitable for analyzing a particular dataset. Meanwhile, proprietary datasets, often having limited information in these portals, are even harder to understand and analyze.

Noticing these shortcomings, emerging research projects are attempting to build structured dataset information resources that address the challenge of dataset discovery and accessibility. For example, the Stanford University School of Medicine established the Center for Expanded Data Annotation and Retrieval (CEDAR) project in 2015 to facilitate researchers’ standard use of metadata by developing an authoring-friendly computational ecosystem for metadata development, evaluation, use, and refinement [4]. By 2017, they had developed a CEDAR Workbench, which was an ontology-assisted tool to help scientific experiment metadata authoring [5]. Meanwhile, the University of California at San Diego is leading the development of a data discovery index system, the biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) [6], to index data that are stored elsewhere to facilitate data integration tasks that adopt content standards and high-level schema. A prototype biomedical data search engine, DataMed [7], under the bioCADDIE project, has included metadata extracted from multiple biomedical data repositories, such as the Cambridge Crystallographic Data Centre (CCDC) and U.S. National Center for Biotechnology Information (NCBI)’s BioProject. Similar to what PubMed (a free search engine that comprises more than 28 million citations from multiple literature databases and resources) has done for the biomedical literature, DataMed aims to make a comparable contribution for biomedical data.

However, the current attempts, focusing on integrating and searching datasets and dataset information, often lack consideration of the learning needs of specific target user populations. Particularly, there is no resource specifically designed to address the needs of health informatics students and novice researchers. Their learning curve is considerably steep when they explore datasets using existing resources. We believe the lack of a healthcare dataset information resource that brings information from various resources together to address the unique needs and questions from these learners is an important gap in health informatics development.

To bridge the gap, we have developed the Dataset Information Resource (DIR) framework, specifically aimed at helping entry-level health informatics students and researchers. For these novices, the challenges are different from established researchers. It is not the discovery of datasets that is important. Rather, the importance lies in the surveying of the landscape of existing datasets and the identification of a proper dataset from the set of common datasets for a given problem. Additionally, the understanding of the dataset and related analytical methods is critically important. The DIR framework does not contain actual data from the datasets. Instead, it is a specialized knowledge base that provides comprehensive knowledge and answers sophisticated questions about noteworthy datasets that address the needs of beginning learners. Besides common information about datasets, such as descriptions, we focus more on knowledge needed by novices, such as analytical methods that datasets can utilize. In this case, novices can quickly obtain a solid understanding through concrete cases. Moreover, we provide dataset blogs in the DIR so that users can easily start data analysis by following sample codes and instructions.

For a flexible, meaningful, and robust knowledge representation, we leveraged Semantic Web [8] technologies. Meanwhile, we incorporated the W3C Dataset Description Profile standard [9] developed by the Semantic Web Health Care and Life Sciences (HCLS) interest group to ensure that the metadata delivered are well defined and organized. The current DIR prototype focuses on 12 representative datasets in healthcare, including both public and proprietary datasets. The prototype is published and accessible via https://cci-hit.uncc.edu/dir/.

Methods

The DIR framework is based on Semantic Web technologies. Building on them, we developed methods to extract knowledge from the datasets as well as existing research articles that had analyzed these datasets. We also developed a question-answering module that answered novice questions that had been posted on the web. In the following sections, we briefly describe the Semantic Web first and then describe the system design, major components, knowledge representation and extraction, and dataset learning of the DIR framework.

Semantic web

The Semantic Web is an extension that adds semantics and logic to the well-known World Wide Web (WWW). In the traditional web pages, entities, such as concepts, are dispersed in the text. They are not clearly identified and their relationships are not explicitly represented. In contrast to traditional web pages, the Semantic Web enhances the regular web by coding and linking important concepts. Therefore, it makes semantics behind data understandable not only to human beings but also to machines. The Semantic Web is based on the Resource Description Framework (RDF) [10]. To link entities, RDF provides a straightforward syntax for describing resources, which is called “triple”. An RDF triple contains three components—the subject, predicate, and the object, where the predicate represents the relationship between the subject and the object. To query the linked entities, a query language, SPARQL Protocol and RDF Query Language (SPARQL) [11], is designed, which is the key to reasoning. With the support of these techniques, a number of RDF-based resource frameworks have already been developed that show the power of the Semantic Web, such as DBpedia [12] and the Neuroscience Information Framework (NIF) [13].

DIR framework overview

The proposed architecture of the DIR system is shown in Fig. 1. It consists of three major components: (1) knowledge representation (requires the ability to represent metadata in a flexible, extendable, and reusable way to meet and surpass the FAIR Data Principles [14]), (2) question answering (delivers exact knowledge to novices), and (3) metadata extraction (extracts metadata tailored to novices from a large number of diverse dataset resources). With these components, the system can integrate and represent knowledge from scattered datasets, allow flexible research questions, and provide precise answers at a suitable level of comprehension.

Proposed architecture of DIR system

The DIR prototype is built on top of the open-sourced Semantic MediaWiki (SMW) platform [15] for knowledge representation and question answering. SMW, which tightly couples traditional web pages with an RDF representation to capture essential knowledge, is an extension of MediaWiki (MW) [16] (see Fig. 2). Additionally, MW is well-known as the foundation of Wikipedia, whose English site contains 5,605,853 articles. Therefore, advantages of MW—such as stability facing massive content and heavy traffic—and advantages of SMW—including the embedded functionality to represent RDF triples by using properties, classes, and semantic forms—can be fully leveraged. Once the knowledge of diverse datasets is extracted, SMW provides a platform for representation and a SPARQL-like mechanism for the semantic query.

Infrastructure of DIR prototype

Knowledge representation in DIR

To represent dataset metadata in a standard manner that is findable, accessible, interoperable, and reusable, we adopt the W3C Dataset Description Profile [9] as the basis of a metadata description model. This profile categorizes dataset metadata in three levels: summary, version, and distribution (see Fig. 3). The summary level is the highest-level description of datasets for the most common information that is independent of specific versions, such as titles, publishers, and homepage links. The version level, as an intermediary of summary and distribution levels, captures version-specific metadata, such as version identifiers and issue dates. The distribution level describes specific forms of a specific version. It includes the most detailed information and guidance, such as data items and links to achieve data. In the DIR prototype, each level of a dataset is a page. Since a dataset can have multiple versions and each version may have various forms, each dataset is described by at least three pages—a summary level (the entrance), at least one version level, and at least one distribution level. For each level, the W3C profile defines a set of suggested data elements, properties, and ranges. The properties that describe datasets are all selected from existing ontologies, such as the Provenance Authoring and Versioning ontology (pav) [17], Data CATalog vocabulary (dcat) [18], and the CItation Typing Ontology (cito) [19]. Since levels depend on each other, several specific properties are defined to link different level pages of a dataset, such as pav:hasCurrentVersion (links the summary level to the version level) and dcat:distribution (links the version level to the distribution level).

Schema of extended W3C dataset description profile

The DIR framework further extends the W3C Dataset Description Profile standard to incorporate properties that represent specific knowledge needed to address the learning needs of health informatics novices. Figure 3 presents the extended properties in bold font, and Table 1 illustrates the detailed extension. As shown in Table 1, four major types of knowledge are currently extended: descriptive information, publication-related metadata, detailed data elements, and blogs. Among publication-related metadata, the Publication-based Popularity Index (PPI) is a special property used to compare and rank datasets [20]. Blogs of each dataset are unique and important metadata in the DIR and elaborate on concrete instructions, sample codes, and results that guide an easy start for practice. These blogs targeting novices are written by experienced dataset users, so direct support is strongly provided.

Table 1

Extended dataset metadata based on W3C dataset description profile

Property | Original value in W3C profile | Extended value in DIR | Level | Description
Descriptive information
dct:accrualPeriodicity | IRI | IRI or xsd:string | Summary level | Dataset update frequency
Patient type | N/A | xsd:string | Summary Level | Patient type in a dataset (e.g., ICU patients)
Geographic area | N/A | xsd:string | Summary level | Geographic area of a dataset (e.g., city, region, and state)
Availability | N/A | xsd:string | Summary level | Availability of a dataset (e.g., public or proprietary)
Dct:source | IRI | IRI or xsd:string | Version level and distribution level | Data source provenance
Subject number | N/A | xsd:integer | Version level | Number of subjects (e.g., number of patients)
Table number | N/A | xsd:integer | Version level | Number of tables
pav:createdWith | IRI | IRI or xsd:string | Distribution level | Tools used to create a dataset
Publication-related metadata
cito:citesAsDataSource | N/A | IRI | Summary level | Link to publications or a collection of publications using a dataset
Publication number | N/A | xsd:integer | Summary level | Number of publications that analyze a dataset
Methods in publications | N/A | xsd:string | Summary level | Methods used in publications to analyze a dataset
Top methods in publications | N/A | xsd:string | Summary level | Top (usually top 10) methods used in publications to analyze a dataset
PPI | N/A | xsd:float | Summary level | A publication-based popularity index for dataset ranking
Detailed data elements
sio:has-data-item | IRI | IRI or xsd:string | Distribution level | Item listing (e.g., tables and entities)
Data elements | N/A | xsd:string | Distribution level | Data elements (e.g., attributes)
Blogs
Blog | N/A | IRI | Summary level | Links to blogs of a dataset

Datasets in current DIR prototype

The current implementation of the DIR includes 12 representative datasets in healthcare, of which 3 datasets—Healthcare Cost and Utilization Project (HCUP) [21], Truven Health MarketScan (MarketScan) [22], and Medical Information Mart for Intensive Care (MIMIC) [23]—are retained from the previous DIR version [24]; nine others are selected from working group notes discussed by domain experts at the UNC Charlotte Health Informatics and Outcomes Research Academy [25]. The nine extended datasets are National Health and Nutrition Examination Survey (NHANES) [26], SEER-Medicare Linked Database (SEER-Medicare) [27], National Longitudinal Study of Adolescent to Adult Health (Add Health) [28], Minimum Data Set (MDS) [29], Clinical Practice Research Datalink (CPRD) [30], The Health Improvement Network (THIN) [31], Premier Healthcare Database (Premier) [32], Clinformatics Data Mart (Clinformatics) [33], and Humedica NorthStar (Humedica) [34].

There are several reasons to choose these datasets. To verify the universality of DIR knowledge representation, they cover most types of healthcare datasets, including claims data (SEER-Medicare, CPRD, MarketScan, Premier, and Clinformatics), electronic medical records (MDS and THIN), hospital data (SEER-Medicare, HCUP, MIMIC, and Humedica), laboratory data (Clinformatics), surveys (NHANES and Add Health), and contextual data (Add Health).

Additionally, these datasets are all large and complex datasets in healthcare, of which three (SEER-Medicare, Add Health, and Clinformatics) even include multiple types listed in the prior paragraph. Most of them have a large number of subjects. For example, HCUP includes the largest collection of longitudinal hospital care data in the United States, and MarketScan consists of nearly 240,000,000 patients’ fully integrated, de-identified, individual-level healthcare claims data. In addition to the large amount of data, the diversity of data and the complexity of the structure make novices more difficult to understand and begin to analyze the datasets. For example, MIMIC contains not only numeric and textual data stored in tabular forms, such as lab results and electronic documentation but also graphical data that are stored separately, such as bedside monitor trends and waveforms. Adopting these graphical signals requires not only a deep understanding of data themselves but also sufficient computer skills to convert them into analyzable data and adequate knowledge to decide analytical methods.

Moreover, these datasets are all widely used in healthcare data analytics. A large number of research articles have been published based on these datasets. By searching in PubMed Central (PMC) [35]—an authoritative electronic archive of free full-text biomedical and life sciences journal articles supported by U.S. National Institutes of Health’s National Library of Medicine (NIH/NLM)—the most studied dataset, NHANES, was mentioned in 37,485 articles, while the least discussed dataset among them, Humedica, was mentioned in up to 22 articles. On average, each dataset contributes to more than 4000 publications in PMC.

Finally, these datasets are representative of both public and proprietary datasets. Among the 12 datasets, two of them (NHANES and MIMIC) are public for research purposes, nine of them (SEER-Medicare, HCUP, MDS, CPRD, MarketScan, THIN, Premier, Clinformatics, and Humedica) are proprietary, and one dataset (Add Health) provides both public- and contractual-use data. In novices’ perspectives, complex proprietary datasets are even more challenging than public datasets because they have difficulty retrieving information elsewhere to help them build up a good understanding accurately and quickly.

In the current implementation, we manually extracted most of the metadata from dataset documentations and semi-automatically extracted metadata about analytical methods from publications. The extracted metadata was first stored in the RDF triple format in Excel spreadsheets and imported into MW, using a Python script that converts spreadsheets to MW importable XML files. To ensure the accuracy of manually extracted metadata, a team of health informatics research assistants was formed to review and correct these metadata iteratively.

Extraction of analytical methods from publications

Data analytical methods that have been successfully applied to datasets are important knowledge for data science learners. To deliver this knowledge, we developed a semi-automatic method to extract various analytical methods that had been used in published articles that analyzed the specific datasets in the DIR. For this task, we first developed an ontology of data analytical methods, Method Ontology (MethodOntology.owl [36]), which extended an existing ontology. Based on the Method Ontology, we developed a rule-based Named Entity Recognition (NER) pipeline to extract instances of analytical methods reported in selected publications.

We used PMC as the data resource and downloaded full-text articles that mentioned the 12 datasets, using the keyword identification method in [20]. In total, 48,282 PDF-format publications were obtained. The publication number of each dataset is shown in Table 2. To preprocess these publications, we developed a pre-processor, written in the Bash command and Python programming language, which included three major steps: (1) converted PDF files to plain text; (2) excluded proceedings and articles that only cited a dataset without analyzing it; and (3) selected relevant content by removing reference sections. After preprocessing, 25,201 publications remained.

Table 2

Publication numbers of 12 datasets

Dataset | # of PDF-format articles in PMC | # for method extraction after preprocessing | # that analyzing datasets
NHANES | 37,485 | 16,213 | 10,674
SEER-Medicare | 2569 | 2276 | 1627
Add Health | 1881 | 1477 | 1028
HCUP | 1785 | 1398 | 993
MDS | 1337 | 1053 | 584
CPRD | 1014 | 735 | 477
MarketScan | 985 | 920 | 614
THIN | 733 | 678 | 434
MIMIC | 237 | 206 | 152
Premier | 165 | 158 | 95
Clinformatics | 69 | 65 | 49
Humedica | 22 | 22 | 9
Total | 48,282 | 25,201 | 16,736

The Method Ontology describes data analytical methods, which include all major machine learning, data mining, and statistical methods. This ontology extends the Data Mining Knowledge Base (DMKB.owl) of the Data Mining OPtimization Ontology (DMOP version 5.4), which was originally designed to support informed decision-making in the data mining (DM) process [37]. The DMKB.owl describes instances of DMOP concepts, including individual algorithms in popular data mining software, such as RapidMiner and Weka. For the method extraction purpose, the Method Ontology extended it by adding and linking new methods, which were extracted in a training set of dataset publications, and synonyms of all method instances. Figure 4 shows the structure of major method classes and a few examples of extended instances in the Method Ontology.

Structure of major method classes and some examples of extended instances in Method Ontology. Extended elements are shown in dashed boxes

A rule-based NER was carried out in the Clinical Language Annotation, Modeling, and Processing Toolkit (CLAMP) [38]—a Natural Language Processing (NLP) software—and was handled by a pipeline that included a sentence detector, a tokenizer, and a dictionary lookup component. The input to this pipeline included the preprocessed publications as well as a method dictionary with semantic labels generated from the Method Ontology. After all potential method entities in the publications were extracted, a post-processor was developed to refine these entities and to combine synonyms for further metadata representation. As a result, method entities were extracted and were represented on summary level pages of the datasets. Assume that a publication that analyzed a dataset mentioned at least one analytical method in the full text. In that case, more than half (16,736 out of 25,201) of the preprocessed publications would have analyzed these datasets. According to the publications, the most frequently used methods for the 12 datasets, as well as proportions of publications that utilized the corresponding method, are shown in Table 3. Among these methods, logistic regression, mentioned in 4229 publications, was the most frequently used (see Fig. 5).

The most frequently used methods in publications of 12 datasets

Table 3

Ten most frequently used methods to analyze each dataset

Dataset | Methods | | | |
NHANES | EM algorithm | Neural network model | Wilcoxon signed-rank test | Poisson regression | Chi-squared test
| 29.55% | 19.63% | 16.69% | 15.02% | 14.85%
| Kruskal-Wallis test | Logistic regression | Log-rank test | Linear regression | T-test
| 14.32% | 12.56% | 12.17% | 10.04% | 8.51%
SEER-medicare | Chi-squared test | Logistic regression | Cox regression | Log-rank test | Survival analysis
| 54.52% | 50.83% | 39.64% | 17.46% | 14.87%
| T-test | Regression model | Kaplan-Meier survival estimates | Linear regression | Propensity score matching
| 11.12% | 10.45% | 9.34% | 8.85% | 7.01%
Add health | Logistic regression | Chi-squared test | Linear regression | Regression model | Principal component analysis
| 50.00% | 33.17% | 13.13% | 9.82% | 8.07%
| ANOVA | Poisson regression | T-test | Propensity score matching | Cox regression
| 7.49% | 5.74% | 5.06% | 3.40% | 3.40%
HCUP | Logistic regression | Chi-squared test | Linear regression | T-test | Regression model
| 57.91% | 48.44% | 20.24% | 18.03% | 15.61%
| ANOVA | Poisson regression | Cox regression | Mann-Whitney U test | Bootstrap
| 9.87% | 9.06% | 7.45% | 7.35% | 4.23%
MDS | Logistic regression | Chi-squared test | Linear regression | Regression model | T-test
| 42.12% | 39.73% | 17.29% | 14.90% | 13.53%
| ANOVA | Cox regression | Mann-Whitney U test | Bootstrap | Survival analysis
| 13.18% | 9.93% | 7.19% | 4.11% | 3.77%
CPRD | Logistic regression | Cox regression | Chi-squared test | Poisson regression | Propensity score matching
| 42.35% | 31.03% | 18.87% | 12.37% | 10.48%
| Linear regression | Regression model | Survival analysis | T-test | Kaplan-Meier survival estimates
| 9.85% | 8.60% | 6.08% | 5.66% | 4.61%
MarketScan | Chi-squared test | Logistic regression | Cox regression | T-test | Poisson regression
| [-2pt]47.88% | 43.32% | 19.22% | 12.87% | 12.21%
| Propensity score matching | Linear regression | Regression model | ANOVA | Fisher’s exact test
| 10.91% | 9.93% | 9.77% | 6.68% | 5.86%
THIN | Logistic regression | Cox regression | Chi-squared test | Poisson regression | Regression model
| 37.33% | 26.04% | 23.27% | 12.44% | 9.91%
| Inverse probability weighting | Linear regression | T-test | Survival analysis | Propensity score matching
| 8.99% | 8.53% | 8.06% | 6.91% | 6.68%
MIMIC | Logistic regression | Chi-squared test | T-test | Mann-Whitney U test | Regression model
| 45.39% | 20.39% | 17.76% | 15.79% | 14.47%
| Support vector machine | Linear regression | Cox regression | Kolmogorov-Smirnov test | K-nearest neighbors
| 14.47% | 11.84% | 11.18% | 9.87% | 9.21%
Premier | Chi-squared test | K-means | Decision tree model | Logistic regression | Propensity score matching
| 41.05% | 38.95% | 27.37% | 21.05% | 14.74%
| Kruskal-Wallis test | Linear discriminant analysis | Regression model | Linear regression | T-test
| 13.68% | 11.58% | 11.58% | 8.42% | 8.42%
Clinformatics | Linear regression | Bootstrap | Regression model | Kruskal-Wallis test | Chi-squared test
| 44.90% | 28.57% | 20.41% | 14.29% | 12.24%
| F-test | Cox regression | Logistic regression | ANOVA | Survival analysis
| 12.24% | 10.20% | 10.20% | 8.16% | 6.12%
Humedica | Chi-squared test | Logistic regression | Bootstrap | Fisher’s exact test | Cox regression
| 33.33% | 22.22% | 22.22% | 22.22% | 11.11%
| T-test | Linear regression | Propensity score matching | Survival analysis | Ensemble learning
| 11.11% | 11.11% | 11.11% | 11.11% | 11.11%

We evaluated the pre-processor and the method extraction steps separately. The results showed that the 95% confidence interval of the pre-processor’s accuracy was [92.26%, 99.39%], and the precision and recall of the analytical method extraction were 93.82% and 90.53%, respectively.

Dataset learning and question answering

Once the dataset knowledge is extracted and represented, the direct way to query the knowledge is to write SPARQL-like queries in the semantic search mechanism provided by SMW. While this direct method is powerful, it requires an understanding of the Semantic Web and SPARQL, which is clearly burdensome to novices. Our current approach to addressing this issue is to offer a simplistic question-answering functionality by identifying the most popular questions that novices ask and providing ready-to-use queries. We created a parameterized question page for each representative question, where users can simply input words and click the Run Query button to obtain precise answers. The list of current parameterized question pages is shown in Table 4.

Table 4

Eighteen parameterized question pages in current DIR

Data-driven questions | Which datasets include some specific information/data elements?
| Which datasets have more than a specific number of subjects?
Method-driven questions | Which datasets can I apply a specific method to?
Introduction questions | What does a dataset talk about?
| How to get a specific dataset?
| What are the methods that publications used with a specific dataset?
| What are the publications using a specific dataset?
| Is a specific dataset open to the public?
| How many subjects are there in a specific dataset?
| How many tables are there in a specific dataset?
| What are the different tables/files in a database?
| What are the data elements in a specific dataset?
| What are the patient types that a specific dataset handles?
| How frequently are data updated in a dataset?
| How many times is a dataset cited?
| Who reports the data in a specific dataset?
| What is the geographic area of a dataset?
| What is the full name of a dataset?

For example, if users are curious about which datasets can successfully utilize the Support Vector Machine, they can simply visit the “Which datasets can I apply the method to” question page, choose or type in “Support Vector Machine,” and click the Run Query button to obtain “Answer: NHANES, CPRD, THIN, HCUP, MDS, MIMIC.” The dataset result is in order based on the PPI recommendation. In this example, the query below has already been embedded in the question page template:

As another example, if users need to investigate large datasets that have more than 1,000,000 subjects, they can refer to the parameterized question page—“Which datasets have more than a specific number of subjects”—that includes the following query:

To determine the most popular questions that novices ask, we analyzed a variety of resources, including a publication that guides novices to conduct high-value dataset analysis [39], questions labeled as “dataset” on question-and-answer sites (e.g., Quora [40] and Stack Exchange [41]), and opinions from health informatics novices through interviews.

Results and discussion

A prototype of the DIR has been developed and released. It is accessible via https://cci-hit.uncc.edu/dir/. The current DIR homepage is shown in Fig. 6. Built on the foundation of the Semantic Web and the extended W3C dataset description profile, we have provided knowledge about 12 representative datasets in healthcare—NHANES, SEER-Medicare, Add Health, HCUP, MDS, CPRD, MarketScan, THIN, MIMIC, Premier, Clinformatics, and Humedica—and five blogs. To facilitate novices’ question answering, 18 ready-to-use questions (Table 4) have been provided. In addition, the more powerful semantic search function is available for users who are familiar with SPARQL. To ease usability, a tutorial and a support page with an issue tracker and a feedback form are also provided.

At the time of this paper’s submission, the DIR prototype contained 264 pages. The average page loading time was 1.44 s. The current approach to add a new dataset includes both manual metadata extractions (from documentations with team review-based quality check) and semi-automatic knowledge extractions (from publications using NLP technologies). To add a new dataset, the current approach takes approximately one day, in general, for manual extractions and a few minutes for semi-automatically extracting analytical methods, excluding the time to collect publications.

We have conducted a survey and collected feedback from 15 target users who were novices in healthcare data research. Of the target users, 40% had a background in health informatics, and 86.7% had a background in data analytics. We asked the subjects to compare Google, DIR, and other resources in seven use cases and also asked for general comments. The survey results indicated that 73.3% of users, on average, preferred the DIR in these use cases. Significantly, 100% of them preferred the DIR in the case of finding datasets that included a particular data element; 93.3% preferred the DIR when they wanted to adopt a specific analytical method; and 86.7% preferred the DIR in the case of discovering large-enough datasets, such as a dataset that had more than 1,000,000 subjects. In terms of more general knowledge, users tended to rely on broader resources. For example, only 60% of users chose the DIR when they were looking for basic descriptions of a dataset or tutorials about gaining access, while others felt more comfortable on searching in Google, browsing the official website, or using both DIR and other resources simultaneously. Overall, the DIR obtained a score of 86.7% in helpfulness, 83.8% in ease of discovering datasets, 82.9% in ease of question answering, and 82.9% in the scale of meeting users’ expectations about healthcare dataset information resources.

According to comments in survey responses, users highlighted the advantages of the DIR as targeted and novice-friendly. As some users commented: “It filters out the irrelevant information and is more structural”; “Beginner-friendly. Information is exhibit[ed] clearly to the user”; and “Sample questions and semantic search are very useful for researchers to find the right dataset or information, or we can say it looks more intelligent than other search engine[s] like [G]oogle.”

However, the DIR clearly has several limitations in this initial phase. (1) The current DIR prototype still relies on manual extractions in part, which is time-consuming and labor-intensive for DIR developers during dataset extending. This limitation has two possible ways to be improved. One refers to the entity linking and typing topic that is intensely discussed in Semantic Web conferences, such as the Open Knowledge Extraction Challenge (OKE) [42] at the European Semantic Web Conference (ESWC). The other way, mentioned by the CEDAR project, involves promoting an authoring-friendly ecosystem in the healthcare dataset community and encouraging researchers to contribute open metadata. (2) Currently, we do not differentiate subclasses of analytical methods, that is, the statistical methods, such as Chi-Square Test, are listed together with machine learning methods, such as Ensemble Learning. Further classification of methods based on the Method Ontology will be needed to address more detailed user questions. (3) As one user commented in the survey: “For now, finding a question is not that hard. However, if the question set becomes larger, then I think it can cause a problem. Somehow you need to facilitate this part,” which reveals that preparing query-embedded question-answering pages can only be a temporary solution. When the system is expanded, a real natural language question-answering functionality should be implemented. Actually, question answering is a stand-alone topic in the Semantic Web community and has been discussed over decades in conferences (e.g., the open challenge on Question Answering over Linked Data (QALD) [43] at ESWC) and publications (e.g., [44–48]). (4) As another user mentioned in the survey: “I’m not sure if researchers will trust the information on DIR.” Rely on simple quality check approaches is one of the limitations. To ensure quality and to gain user trust, a systematic quality assurance method needs to be developed and reported.

Conclusions

We conclude that it is feasible to develop a DIR that provides value for entry-level health informatics students and researchers. Knowledge about datasets is effectively represented in Semantic Web technologies. At this stage, the DIR has already been able to provide comprehensive and relevant knowledge of 12 important healthcare datasets, which is expected to improve health informatics novices’ ability to learn data analysis using suitable datasets.

In contrast to bioinformatics datasets, of which most data elements have already been represented in RDF at the knowledge level, the DIR will continue focusing on the healthcare datasets that are usually at a lower level granularity.

Further development is underway to improve efficiency, accuracy, and scalability. Suitable directions for expansion include two levels: content and function. The content level adds more healthcare datasets, identifies more types of knowledge for target users, and involves a systematic quality assurance method to ensure the quality of metadata. The function level includes developing a natural language-based question-answering component, more automated methods to extract knowledge, intelligent functionalities to compare similar datasets, and collaborative features, such as discussion forums that allow users to help each other and suggest new content.

Acknowledgements

We would like to show our gratitude to our colleagues in the Health Informatics Lab at UNC Charlotte: Madhusudhan Vaddi, Pujitha Gangarapu, Lohitaksh Yogi, Shweta Rajaram Patil, Saugat Karki, and Jessica Yin, for their hard work in the manual metadata extraction and review; and Ishita Roychowdhury, for her important contribution in annotating analytical methods in research articles.

Funding

Publication costs were funded by faculty research support.

Abbreviations

Add Health | National longitudinal study of adolescent to adult health
bioCADDIE | Biomedical and healthcare data discovery index ecosystem
CCDC | Cambridge crystallographic data centre
CEDAR | Center for expanded data annotation and retrieval
cito | CItation typing ontology
CLAMP | Clinical language annotation, modeling, and processing toolkit
Clinformatics | Clinformatics data mart
CPRD | Clinical practice research datalink
dcat | Data catalog vocabulary
DIR | Dataset information resource
DM | Data mining
DMKB | Data mining knowledge base
DMOP | Data mining optimization ontology
ESWC | European semantic web conference
FAIR | Findable, accessible, interoperable, and re-usable
HCLS | Health care and life sciences
HCUP | Healthcare cost and utilization project
Humedica | Humedica NorthStar
MarketScan | Truven health MarketScan
MDS | Minimum data set
MIMIC | Medical information mart for intensive care
MW | MediaWiki
NCBI | National center for biotechnology information
NER | Named entity recognition
NHANES | National health and nutrition examination survey
NIF | Neuroscience information framework
NIH | National institutes of health
NLM | National library of medicine
NLP | Natural language processing
OKE | Open knowledge extraction challenge
pav | Provenance authoring and versioning ontology
PMC | PubMed central
PPI | Publication-based popularity index
Premier | Premier healthcare database
QALD | Question answering over linked data
RDF | Resource description framework
SEER-Medicare | SEER-medicare linked database
SGIM | Society of general internal medicine
SMW | Semantic MediaWiki
SPARQL | SPARQL protocol and RDF query language
THIN | The health improvement network
WWW | World wide web

Authors’ contributions

JS, MZ, LY, and YG contributed to the design of the research. JS developed the system, analyzed the results, and wrote the manuscript. YG supervised the research. All authors read, edited, and approved the final manuscript.

Notes

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Jingyi Shi, Email: ude.ccnu@5ihsj.

Mingna Zheng, Email: moc.liamg@nmgnehz.

Lixia Yao, Email: ude.oyam@aixil.oay.

Yaorong Ge, Email: ude.ccnu@egy.

References

2. Data ∣ Centers for Disease Control and Prevention. https://data.cdc.gov/. Accessed 5 Apr 2018.

4. Musen MA, Bean CA, Cheung K-H, Dumontier M, Durante KA, Gevaert O, Gonzalez-Beltran A, Khatri P, Kleinstein SH, O’Connor MJ, Pouliot Y, Rocca-Serra P, Sansone S-A, Wiser JA, Team atC. The center for expanded data annotation and retrieval. J Am Med Inform Assoc. 2015;22(6):1148–52. [PMC free article] [PubMed] [Google Scholar]

5. Gonçalves Rafael S., O’Connor Martin J., Martínez-Romero Marcos, Egyedi Attila L., Willrett Debra, Graybeal John, Musen Mark A. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2017. The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments; pp. 103–110. [Google Scholar]

6. bioCADDIE | Biomedical and healthCAre Data Discovery and Indexing Ecosystem. https://biocaddie.org/. Accessed 3 Mar 2017.

7. Ohno-Machado L, Sansone S-A, Alter G, Fore I, Grethe J, Xu H, Gonzalez-Beltran A, Rocca-Serra P, Gururaj AE, Bell E, Soysal E, Zong N, Kim H-E. Finding useful data across multiple biomedical data repositories using DataMed. Nat Genet. 2017;49(6):816–9. doi: 10.1038/ng.3864. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Berners-Lee T, Hendler J, Lassila O. The semantic web. Sci Am. 2001;284(5):28–37. doi: 10.1038/scientificamerican0501-34. [CrossRef] [Google Scholar]

9. Dumontier Michel, Gray Alasdair J.G., Marshall M. Scott, Alexiev Vladimir, Ansell Peter, Bader Gary, Baran Joachim, Bolleman Jerven T., Callahan Alison, Cruz-Toledo José, Gaudet Pascale, Gombocz Erich A., Gonzalez-Beltran Alejandra N., Groth Paul, Haendel Melissa, Ito Maori, Jupp Simon, Juty Nick, Katayama Toshiaki, Kobayashi Norio, Krishnaswami Kalpana, Laibe Camille, Le Novère Nicolas, Lin Simon, Malone James, Miller Michael, Mungall Christopher J., Rietveld Laurens, Wimalaratne Sarala M., Yamaguchi Atsuko. The health care and life sciences community profile for dataset descriptions. PeerJ. 2016;4:e2331. doi: 10.7717/peerj.2331. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

10. Miller E. An introduction to the resource description framework. Bull Am Soc Inf Sci Technol. 1998;25(1):15–9. doi: 10.1002/bult.105. [CrossRef] [Google Scholar]

11. Prud’Hommeaux E, Seaborne A. SPARQL query language for RDF. W3C Recomm. 2008; 15.

12. Mendes PN, Jakob M, Bizer C. DBpedia: A Multilingual Cross-domain Knowledge Base,. In: LREC: 2012. p. 1813–7.

13. Larson SD, Martone ME. NeuroLex.org: an online framework for neuroscience knowledge. Front Neuroinformatics. 2013; 7(18). [PMC free article] [PubMed]

14. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE. The FAIR Guiding Principles for scientific data management and stewardship. Sci. 2016; 3. [PMC free article] [PubMed]

15. Krötzsch Markus, Vrandečić Denny, Völkel Max. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006. Semantic MediaWiki; pp. 935–942. [Google Scholar]

17. Ciccarese P, Soiland-Reyes S, Belhajjame K, Gray AJ, Goble C, Clark T. PAV ontology: provenance, authoring and versioning. J Biomed Semant. 2013;4(1):37. doi: 10.1186/2041-1480-4-37. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. Shi J, Zheng M, Yao L, Ge Y. A Publication-based Popularity Index (PPI) for Healthcare Dataset Ranking. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI): 2018. p. 247–54. 10.1109/ICHI.2018.00035. [CrossRef]

21. Steiner C, Elixhauser A, Schnaier J. The healthcare cost and utilization project: an overview. Eff Clin Pract: ECP. 2001;5(3):143–51. [PubMed] [Google Scholar]

23. Johnson AEW, Pollard TJ, Shen L, Lehman L-wH, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035. doi: 10.1038/sdata.2016.35. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Shi J, Zheng M, Yao L, Ge Y. DIR - A semantic information resource for healthcare datasets. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): 2017. p. 805–10. 10.1109/BIBM.2017.8217758. [CrossRef]

25. Health Informatics and Outcomes Research Academy | UNC Charlotte. https://hinora.uncc.edu/. Accessed 1 Apr 2017.

37. Keet CM, Ławrynowicz A, d’Amato C, Kalousis A, Nguyen P, Palma R, Stevens R, Hilario M. The Data Mining OPtimization Ontology. Web Semant Sci Serv Agents World Wide Web. 2015;32:43–53. doi: 10.1016/j.websem.2015.01.001. [CrossRef] [Google Scholar]

38. Soysal Ergin, Wang Jingqi, Jiang Min, Wu Yonghui, Pakhomov Serguei, Liu Hongfang, Xu Hua. CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association. 2017;25(3):331–336. doi: 10.1093/jamia/ocx132. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

39. Smith AK, Ayanian JZ, Covinsky KE, Landon BE, McCarthy EP, Wee CC, Steinman MA. Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources. J Gen Intern Med. 2011;26(8):920–29. doi: 10.1007/s11606-010-1621-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

40. Quora - The Best Answer to Any Question. https://www.quora.com/. Accessed 3 Aug 2017.

42. Speck René, Röder Michael, Oramas Sergio, Espinosa-Anke Luis, Ngonga Ngomo Axel-Cyrille. Semantic Web Challenges. Cham: Springer International Publishing; 2017. Open Knowledge Extraction Challenge 2017; pp. 35–48. [Google Scholar]

43. Usbeck Ricardo, Ngomo Axel-Cyrille Ngonga, Haarmann Bastian, Krithara Anastasia, Röder Michael, Napolitano Giulio. Semantic Web Challenges. Cham: Springer International Publishing; 2017. 7th Open Challenge on Question Answering over Linked Data (QALD-7) pp. 59–69. [Google Scholar]

44. Lopez V, Uren V, Sabou M, Motta E. Is question answering fit for the semantic web?: a survey. Semant Web. 2011;2(2):125–55. [Google Scholar]

45. Jeon J, Croft WB, Lee JH. Finding similar questions in large question and answer archives. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management: 2005. p. 84–90.

46. Androutsopoulos I, Ritchie Gd, Thanisch P. Natural language interfaces to databases – an introduction. Nat Lang Eng. 1995;1(01):29–81. doi: 10.1017/S135132490000005X. [CrossRef] [Google Scholar]

47. Hirschman L, Gaizauskas R. Natural language question answering: the view from here. Nat Lang Eng. 2001;7(4):275–300. doi: 10.1017/S1351324901002807. [CrossRef] [Google Scholar]

48. Shekarpour S, Lukovnikov D, Kumar AJ, Endris K, Singh K, Thakkar H, Lange C. Question Answering on Linked Data: Challenges and Future Directions. arXiv:1601.03541 [cs]; 2016. arXiv: 1601.03541.

Articles from BMC Medical Genomics are provided here courtesy of BioMed Central

# data[].attributes.attachment_link.title
Developing a healthcare dataset information resource (DIR) based on Semantic Web

# data[].attributes.attachment_link.description
The right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses ...

# data[].attributes.body
What I enjoyed most this week was learning the capability of remote patient monitoring. Many, including myself, associate virtual health visits as the main use in telemedicine. With these devices, providers are able to have a better understanding of how their patients are doing via an objective standpoint such as blood glucose trends, heart rhythm/rate monitors, and blood pressure monitors. The EHR systems implemented in primary care work together with the third party programs to access all of these results within the patient chart after being reviewed by either a home health agency or a remote diagnostic testing facility. Personally, I have benefitted greatly from the education portion through telemedicine and the EHR. Even as a healthcare worker it is easy to get overwhelmed with the information given or instructions given with your primary care provider. Being able to go back to the provider's note and instruction as well as the listed 3rd party for further instruction is so helpful. For example, times when I have been in physical therapy I have been able to access instructions through my patient portal of what exercises I need to be completing as well as the third party instructional videos through the app.

# data[].attributes.body
https://youtu.be/y02nSCYl2ZE
This video is a great example of how telehealth can bridge the gap for lack of accessibility to healthcare, financial burden, and time. Jennifer's story allows us to have a better understanding of how beneficial telehealth can be for our patients.

# data[].attributes.attachment_link.title
Jennifer's Story - How Telemedicine works

# data[].attributes.body
A continued problem of organized nationwide or even global healthcare comes to the forefront. In order to efficiently use telehealth, all of our healthcare systems need to be able to communicate with each other as well as have the same medical vocabulary.
Another concern with telehealth is the lack of physical face to face contact from patient to provider. Although video calls have closed with they can of this gap in access versus phone calls, the face to face with a provider proves to be more beneficial. This is why I believe that telehealth should never replace physical appointments but rather be used when access is a problem either because of distance, time, etc.

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This journal discusses helpful tools for deciphering whether it is appropriate to use telehealth with a specific patient.

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A great example of how gamification is used everyday in healthcare is the implementation of the health app on apple smart watches. It creates motivation to "close exercise rings" as well as compete against friends.

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Gamification is not the cure-all, but it can't hurt. Introducing fun and games to healthcare apps can motivate patients and collect essential data. After all, it helps to make informed decisions on daily activities that contribute to one's health.

What is gamification

Gamification implies integrating game mechanics and design techniques into non-game experiences. This process motivates audiences participation and engagement while making mundane tasks more fun and interactive.

For more information on gamification as a whole and the mechanisms behind its effectiveness, see our article.

In the context of health IT, gamification is typically employed in medication adherence, medical education-related simulations, fitness and wellness apps. The strategy is to use rewards for users who complete mandated tasks, and typically works in the following ways:

By filling a progress bar to measure success. Thus, developers invoke progress-related instinct.

'An estimated 50% of patients with chronic diseases do not follow the prescribed treatment. Gamified health tracking creates an environment that keeps the patient from straying from the appropriate therapy path.'
Dr. Bertalan Meskó, Director at the Medical Futurist Institute

By allowing users to share progress and results with their friends/other players or designing an "honor roll". Thus, developers create a competitive spirit to stimulating the use of the service.

Fitness App. Reword Unlock by Olha Hurenko
Source:dribbble.com/shots/4492657-Fitness-App-Award-Unlocked

By awarding points, medals, stars, achievement badges or giving virtual currency during each stage of progress. Thus, developers create a sense of accomplishment and increasing motivation levels.

Kenko Health Avatar by Yoann Baunach
Source: dribbble.com/shots/4288089-Kenko-The-avatar-of-your-health

In specialized health apps targeted to older users, individuals with movement or sensory impairments, gamification experiences are created using real-time biofeedback from motion-capture sensors and gesture-control technology.

An often overlooked benefit of gamified healthcare applications is their potential for gathering relevant patient data. Software like this motivates users to give more feedback which, in turn, helps companies find trends, make products that address the needs of the target audience better, and even create new business models. However, due to legal restrictions in countries like Germany and France, gathering data requires the attention of the corporate lawyers, as well as developers. Moreover, it presents an ethical and a cybersecurity challenge.

Gamified healthcare is a big deal: one report predicts the market for it to reach 4.2 billion dollars by 2022. Another one forecasts it to grow to a whopping 13.5 billion dollars by 2025.

Look how gamification techniques can be used in e-learning projects. Or get help to implement game elements in your app.

Gamifying healthcare: case studies

The move to gamification of healthcare, however, seems to be a welcome one. According to PwC's Top Health Industry Issues of 2017 report, 78% of respondents aged between 25 and 44 said they would use some form of gamification in their treatment.
What software healthcare solutions are more relevant?

Fitness and nutrition apps

Self-management chronic condition and medication apps

Healthcare apps for kids

Physical therapy and rehabilitation apps

Emotional health apps

Motivating Wheelchair-bound Patients to Exercise

Many people suffering from spinal trauma or dysfunction and having to use a wheelchair are also at risk of cardiovascular diseases. Exercise is difficult for them and the fact that they are sitting for most of the time only exacerbates the situation. Daily activities don't help maintain the necessary level of activity.

Fortunately, there is GameWheel - an interface that allows connecting wheelchair to the computer as a controller in specialized games.

According to a study by scientists from several American universities, it proved effective in both motivating the patients to exercise more and in making the periods of exercise more productive. The study participants used GameWheel to play a racing game where pushing their physical push on the wheel translated to the speed of the car on the screen.

As a result, the heart rate, oxygen consumption, and ventilation were higher in the players than in their non-playing counterparts. Moreover, some reported that they were so absorbed in the game, that they forgot they were exercising.

Helping Cancer Patients

The "Re-Mission" game series has proven to be effective in helping children and young adults suffering from various forms of cancer.

Re-Mission: Nanobot's Revenge

Not taking their medicines on time is a widespread problem in patients - up to 50% of them either fail to take the drugs regularly or don't file the prescription at all. In the case of life-threatening diseases, it becomes extra important to solve. That's why a non-profit HopeLab Foundation has commissioned a serious game to address the issue.

"Re-Mission" put the player in control of the "Roxy" nanobot that was to fight cancer with chemoblaster, radiation gun, antibiotic rocket, and other weapons derived from actual medical treatments. It proved to be a huge success, so "Re-Mission 2", a suite of free-to-play online games was launched.

These games improved the treatment adherence rates, and also increased the patients' self-efficacy - confidence that the disease can be defeated.

Fitness and nutrition apps

Apple's 2014 App Store review of 100+ health apps proved a direct correlation between gamification elements embed and high user ratings. MyFitnessPal used the highest number of gamification techniques.

We all know Fitbit as one of the early innovators in the wearables game. However, the company is positioning itself as the go-to device for employers. Fitbit has almost 1.500 corporate wellness program customers including BP, IBM, and Bank of America. Most of them give their employees Fitbit devices to track their workout progress and health habits.

Source: play.google.com/store/apps/details?id=com.fitbit.FitbitMobile

Fitbit is an example of how corporate partners are becoming more involved in mHealth apps. Such tactics, therefore, allows employers to reduce employee healthcare costs by improving lifestyles or providing instant access to non-emergency care.

However, one of the most striking examples of how companies accepting healthcare gamification is Apple. They award Apple Watch and iPhone users with badges for accomplishing workout tasks like hiking and cycling or surpassing daily totals like calories burden.

Source: macworld.co.uk/how-to/apple/apple-watch-activity-achievement-badge-3658788

Awarding badges is part of Apple's continued push into healthcare. The Health app on iPhone, Workout app on Apple Watch and Activity apps on both have distinct functions but can define user's health status in details. The strategy helped Apple boost Apple Watch sales and own 2017 wearables market.

Source: imore.com/apple-watch-and-activity-tracking-what-you-need-know

Unlocking wellness achievements turns into a naturally popular behavior. The wild success of Pokémon Go demonstrated how willingly people play achievement-oriented games simply for the fun of earning points. Importantly, Pokémon Go proved that game playing is not always about passive experience - players are required to walk around and keep moving.

Yet Pokémon Go does get people moving more but the effect doesn't last. In fact, the market offers more than 9.000 healthcare-related apps. Many of them are downloaded, used once or twice, then forgotten.

The exercise-tracking startup Pact pursues a highly-motivating policy among people looking to improve their health. Users make pacts promising to exercise or eat healthier.

Source: play.google.com/store/apps/details?id=com.gympact.android&hl=en

By failing to meet their target, users have to pay a monetary penalty between $5 and $50, while those who succeed get a part of the payment. Smartphone location data and photos taken in gym serve as the evidence.

Players can specify how much they would be fined if they failed to meet a pact. This money then goes to a collective pool that allocates payouts among those who do reach their goals.

The powerful driving force of this mHealth app is that users can actually lose money when they fall off track. Unfortunately, even when players held up their part of the pact, the company allegedly failed to provide the funds promised. Pact must return about $1M as of September 2017.

Self-management chronic condition and medication apps

By helping patients understand their chronic conditions better and by simplifying medication management using gamification, patient compliance rates can be increased to achieve better outcomes.

Gamification techniques can make the tedious and repetitive tasks of treating a chronic illness rewarding and more engaging.

Diabetes is considered the "the disease of the 21st century". The 2016 study revealed that many experts support the idea of creating an enjoyable experience for patients living with this chronic disease.

'Naturally people like to be rewarded. Thus, if this [gamification] is applied to the self-management of diabetes, it would be very effective. [...] it will change the view and the experience of self-management of diabetes for the patient.'
from Gamifying Self-Management of Chronic Illness: A Mixed-Methods Study

'Positive reward is enjoyable in whichever form it comes. This will help patients' self-esteem.'
from Gamifying Self-Management of Chronic Illness: A Mixed-Methods Study

Indeed, having diabetes requires self-management skills vital to prevent the complications associated with the disease and maintaining the healthy life.

Gamified apps can help patients self-manage in a more efficient and entertaining manner. They also give them the opportunity to be appreciated for their efforts and to positively compete with one another.

Now, what gamification elements are most commonly used?

Tracking measures of blood glucose, insulin, food intake, and other related info.

Getting feedback based on the entries.

Being notified when blood glucose measures fluctuate.

Glooko provides a remote patient monitoring platform for diabetes that enables users to connect any glucose meter, insulin pump or CGM. The company also offers a FDA-approved app to help patients manage their care and control outcomes.

Source: play.google.com/store/apps/details?id=com.glooko.logbook

mySugr is an another example of a gamified solution for diabetes management. The company is remarkable that they developed a separate app for children ( mySugr Junior app).

Mango Health mobile app reminds patients when it time to take their medicines and records each dose. It also automatically warns users about dangerous interactions between drugs and supplements or with food and drink. By taking medications properly, patients earn points to be redeemable for gift cards or charitable donations.

Source: play.google.com/store/apps/details?id=com.mangohealth.mango&hl=en

Unfortunately, gamification cannot diminish the seriousness of diabetes or any other chronic disease. Patients need help not only to enhance their illness self-management but also to be understood and supported by other victims.

Gamified healthcare apps for kids

Younger users usually do not understand the importance of long-term therapies or medications, regarding their illnesses as short-run miseries. They do not want to swallow bitter pills or have shots, do not want to be in therapy or stay in the hospital.
Gamification can help children forget they undergoing medical treatment, teach them responsibility for their health.
Inspired by Minecraft, Pfizer (a pharmaceutical company) launched a video game aimed at educating younger hemophilia patients, aged 8 to 16, about the importance of adhering to their treatment plans.

Hemocratf is a simulated environment where players interact with the "village doctor" to learn how their treatments work. Kids are challenged to monitor factor levels and self-infuse to help control bleeding if needed.

Zamzee (acquired by Welltock) developed an activity tracker and rewards system for children to get them moving and complete quests based on their physical level. In a randomized controlled study, kids using Zamzee were nearly 60% more active.

Young players collect points by moving and completing challenges. Earned points can be exchanged for virtual rewards, like equipment for their on-site avatars, or physical rewards, like pink duct tape sent to their address.

Physical therapy and rehabilitation apps

After a serious injury, it is difficult and time-consuming to reach even an agreeable level of independence regarding movement or other activity. Gamification takes a chance to reimagine the physical therapy experience.

Reflexion Health offers a patient-facing telerehabilitation solution known as VERA. This platform controls the movements of patients practicing physical therapy exercises. The system works in patients' homes allowing them to watch an animated instructor model on TV or PC.

Motion tracking technology compares patients' performance with the sample and gives guidance and correction suggestions if needed. As VERA helps patients recover function over time, it is essential to encourage, measure and report patient engagement and objective performance of their progress.

The GlassOff program is developed to eliminate dependency on reading glasses by enhancing users brain's image processing function. The recovery process consists of several sessions that are mini visual recognition games.

Working through GlassOff exercises takes about 12 minutes. It's recommended to follow the program 3 times a week for 3 months. The app automatically reminds users when it's time for the next session.

Source: play.google.com/store/apps/details?id=com.glassesoff.android

Emotional health apps

Happify toolset helps users improve their emotional well-being, overcome stress and anxiety that have a negative impact on daily life. Their app has 30+ tracks to choose from and tracks user progress to see how their skills compare. Each track is based on scientific research from neuroscientists and psychologists at Harvard, Stanford or Penn.

Source: play.google.com/store/apps/details?id=com.happify.happifyinc&hl=en
Looking for an attractive healthcare business model? Find your inspiration in our articles:
How can gamification help your business

Employers, insurers, and healthcare providers are focusing more energy on keeping people out of the hospital by helping them manage their own health. Thus, the market demands high-quality and complex solutions that make getting healthy more fun.

Gamification delivers proven and tangible results. By applying gamification elements into the product, businesses have experienced an increase in engagement across social media and website traffic generated.

To bear all the valuable fruits of gamification, businesses have to understand the environment to which it is applied. In other words, specific gamification techniques need to be tailored and adapted by this specific audiences.

Already in favor of gamification in your healthcare app? Fill our online "get a quote" form to start.

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Gamification in Healthcare: the Value of Fun

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Gamification is not the cure-all, but it can't hurt. Introducing fun and games to healthcare apps can motivate patients and collect essential data. After all, it helps to make informed decisions on daily activities that contribute to one's health. Gamification implies integrating game mechanics and design techniques into non-game experiences.

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Creating a gaming platform can lead to high costs. Development with proper research, correct information, and easy accessibility to the user can cost a lot of money. Ultimately, finances are the largest component to obstacles in the healthcare field. To create something on a large scale it requires fundings and a lot of time. If you don't do it correctly it may not even engage the users and turn out to be more a quiz that patients don't want to use.

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Gamification is a tool used in many aspects in our daily lives to get users involved. One example is the application of the apple watch and fitness app. "Closing rings", setting goals, and competing against friends and family keep the user engaged and motivated in their health journey. Weight watchers uses an app to keep people engaged in tracking points in a fun way. You are able to stay connected to your goals while completing milestones on the app and therefore reaping benefits through the app. The asynchronous work this week touched on understanding disease processes through games on the smart phone to help bridge the gap of a complicated pathophysiology and patient knowledge of what is happening in their body as well as better understanding treatment.

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Journal of Clinical Nursing - 2022 - Akturan - Evaluation of alarm fatigue of nurses working in the COVID‐19 Intensive Care

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Alerts and alarms in the healthcare system have created this sense of "alarm fatigue" in healthcare workers. For example, all day long nurses in the hospital hear alarms for patient monitors, IV pumps, or other machine used at the bedside. How do we differentiate when alarms need to be addressed quickly? There needs to be a better way to distinguish alarm significance. Another concern that exists are pop up alerts either on the EHR or pyxis. For example, when tylenol is removed there is an alert that shows the max dose for 24 hours. This creates another window for us to close out before taking the medication but it shows up for every dose of tylenol without incorporating specific patient information such as weight, dose, or last administration. I don't feel it is helpful in this situation by how it is displayed.

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Week 10: Quality and Unintended Consequences

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Week 10: Quality and Unintended Consequences

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https://youtu.be/juitFz2eJHc

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Copyright © 2015 Institute for Healthcare ImprovementAll rights reserved. Individuals may share these materials for educational, not-for-profit uses, provided that the contents are not altered in any way and that proper attribution is given to IHI as the source of the content.

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Week 10: Quality and Unintended Consequences

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Week 10: Quality and Unintended Consequences

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Alarm and alert fatigue is a real thing. Everyday we see the same messages pop up on our EHR/MAR when taking care of patients. It is easy to get lost in just ignoring messages and overriding warnings especially when we are busy (which is pretty much everyday). It is vital to patient safety for each of us to take these alerts seriously and take the time to review what is being presented to us. Alarm fatigue is another obstacle we face. All day long our monitors, IV pumps, and other machines make noise and it becomes background noise in our brains. Some of us go home and hear it in our sleep. Our healthcare system needs to do a better job of alerting to more serious concerns in a way to get our attention. We can continue to get education about alarm fatigue and needing to pay attention but ultimately we all are victim to this problem and now we need to do something different to help identify larger more pressing issues that arise.

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The 3 A's are vital in providing security within the healthcare system, especially when converting to an Electronic Health Record system.
The first A stands for Authentication and represents that the individual is who they say they are. Each person should have their own login information to the healthcare portal. No doctor should give their login to any other individual.
The second A represents Authorization. This topic comes up frequently as a concern when individuals are able to access charts of patients when they are not a part of their healthcare team. No person should look through records that they are not authorized to be in. This includes electronic charts.
The final A is Accountability. When being alerted to important healthcare information such as lab results, red flags alerts for medication issues, or radiographic results, it is the users responsibility to pass on this information to the correct individual or follow up as appropriate.

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

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Cybersecurity in Healthcare Laws and Regulations

The Health Insurance Portability and Accountability Act ( HIPAA) is a federal requirement in the U.S. which applies to covered entities and business associates. HIPAA consists of the HIPAA Privacy Rule, Security Rule and Breach Notification Rule. Covered entities include health plans, healthcare clearinghouses, and healthcare providers who electronically transmit any health information in connection with transactions for which the U.S. Department of Health and Human Services has adopted standards.

Examples of covered entities include physician practices, ambulatory surgical centers, hospitals, long-term care facilities, health plans, healthcare clearinghouses, among others. Business associates perform functions or services on behalf of covered entities. Business associates may create, receive, transmit, or maintain protected health information on behalf of the covered entity. Examples of business associates include accountants, attorneys, cloud service providers, document storage companies, third party billing services and others.

The HIPAA Privacy Rule, 45 CFR Part 160 and Subparts A and E of Part 164 , sets forth permitted and required uses and disclosures of protected health information. The protected health information may exist in any form, including on paper, film and in electronic form. Protected health information is a form of individually identifiable health information.

The HIPAA Security Rule, 45 CFR Part 160 and Part 164, Subparts A and C, sets forth requirements for electronic protected health information. In other words, the confidentiality, integrity and availability of electronic protected health information must be maintained by covered entities and their business associates.

The HIPAA Breach Notification Rule, 45 CFR §§ 164.400-414, requires HIPAA covered entities and their business associates to provide notification following a breach of unsecured protected health information.

A breach is, generally, an impermissible use or disclosure under the Privacy Rule that compromises the security or privacy of the protected health information. An impermissible use or disclosure of protected health information is presumed to be a breach unless the covered entity or business associate, as applicable, demonstrates that there is a low probability that the protected health information has been compromised based on a risk assessment of at least the following factors:

The extent to which the risk to the protected health information has been mitigated

The nature and extent of the protected health information involved, including the types of identifiers and the likelihood of re-identification

The unauthorized person who used the protected health information or to whom the disclosure was made

Whether the protected health information was actually acquired or viewed

Covered entities and business associates, where applicable, have discretion to provide the required breach notifications following an impermissible use or disclosure without performing a risk assessment to determine the probability that the protected health information has been compromised.

There are three exceptions to the definition of breach:

1. The covered entity or business associate has a good faith belief that the unauthorized person to whom the impermissible disclosure was made, would not have been able to retain the information.

2. The inadvertent disclosure of protected health information by a person authorized to access protected health information at a covered entity or business associate to another person authorized to access protected health information at the covered entity or business associate, or organized healthcare arrangement in which the covered entity participates. In both cases, the information cannot be further used or disclosed in a manner not permitted by the Privacy Rule.

3. The unintentional acquisition, access, or use of protected health information by a workforce member or person acting under the authority of a covered entity or business associate, if such acquisition, access, or use was made in good faith and within the scope of authority.

Similar to a covered entity, a business associate also is directly liable and subject to civil penalties for failing to safeguard electronic protected health information in accordance with the HIPAA Security Rule. Additionally, covered entities must establish business associate agreements with their business associates.

A business associate agreement is a written contract between a covered entity and a business associate which must address the following:

1. Authorize termination of the contract by the covered entity if the business associate violates a material term of the contract

2. Establish the permitted and required uses and disclosures of protected health information by the business associate

3. Provide that the business associate will not use or further disclose the information other than as permitted or required by the contract or as required by law

4. Require the business associate to implement appropriate safeguards to prevent unauthorized use or disclosure of the information, including implementing requirements of the HIPAA Security Rule with regard to electronic protected health information

5. Require the business associate to report to the covered entity any use or disclosure of the information not provided for by its contract, including incidents that constitute breaches of unsecured protected health information

6. Require the business associate to disclose protected health information as specified in its contract to satisfy a covered entity's obligation with respect to individuals' requests for copies of their protected health information, as well as make available protected health information for amendments (and incorporate any amendments, if required) and accountings

7. To the extent the business associate is to carry out a covered entity's obligation under the Privacy Rule, rRequire the business associate to comply with the requirements applicable to the obligation to the extent the business associate is to carry out a covered entity's obligation under the Privacy Rule

8. Require the business associate to make available to HHS its internal practices, books and records relating to the use and disclosure of protected health information received from, or created or received by the business associate on behalf of, the covered entity for purposes of HHS determining the covered entity's compliance with the HIPAA Privacy Rule

9. Require, at termination of contract if feasible, the business associate to return or destroy all protected health information received from, or created or received by the business associate on behalf of, the covered entity

10. Require the business associate to ensure that any subcontractors it may engage on its behalf that will have access to protected health information agree to the same restrictions and conditions that apply to the business associate with respect to such information

11. Authorize termination of the contract by the covered entity if the business associate violates a material term of the contract.

HIPAA is enforced by the U.S. Department of Health and Human Services Office for Civil Rights (OCR). OCR has the authority to interpret and enforce HIPAA. Accordingly, it is best for to keep up with guidance from OCR as it relates to the interpretation and enforcement of HIPAA.

42 CFR Part 2

42 CFR Part 2 protects patient records created by federally funded programs for the treatment of substance use disorder. At the state level, healthcare provider organizations must also be aware of other applicable privacy and security laws. Certain special types of health information are deemed to be super protected health information under state law. Examples include records related to drug and alcohol abuse, HIV-related information, and the like.

Section 5 of Federal Trade Commission Act

Section 5 of the Federal Trade Commission (FTC) Act applies to entities other than not-for-profit entities. Specifically, Section 5 of the FTC Act provides that "unfair or deceptive acts or practices in or affecting commerce... are... declared unlawful." The failure of an organization to adequately safeguard computer systems and infrastructure may be deemed to be "unfair" according to the FTC. Additionally, a deceptive practice is one in which consumers are falsely misled into believing that their privacy and/or security of their information is safeguarded.

European Union General Data Protection Regulation

Any organization that processes the personal data of data subjects (people) in the European Union must comply with the General Data Protection Regulation (GDPR). "Processing" includes data collection, storage, transmission, analysis and the like. "Personal data" is any information that relates to a person, such as without limitation names, email addresses, IP addresses, eye color and the like-essentially, any personally identifiable information or information that may be identifying of an individual.

GDPR is broader than HIPAA and regulates any information that may be used, directly or indirectly, or identify a person that is in the European Union. In other words, GDPR applies to many types of personal information, whereas HIPAA regulates protected healthcare information. Even if an organization is located outside of the European Union, GDPR may still apply if:(1) the organization offers goods or services to individuals within the European Union (whether or not a payment is required from them) or (2) the behavior of individuals in the European Union is monitored by the organization. Additionally, GDPR applies to both controllers and processors. A "controller" means a natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data. A "processor" means a natural or legal person, public authority, agency or other body which processes personal data on behalf of the controller.

In order to lawfully process the information of a data subject, there must be a lawful basis for doing so. Lawful bases include the following:

1. Consent from the data subject

2. Performance of a contract, where the processing of the data is necessary

3. Where the processing is necessary for the purposes of the legitimate interests pursued by a controller or processor

Personal Information Protection and Electronic Documents Act

The Personal Information Protection and Electronic Documents Act (PIPEDA) applies to private-sector organizations across Canada that collect, use or disclose personal information in the course of a commercial activity. Hospitals are generally governed by provincial laws in Canada, but PIPEDA may apply in certain instances.

Certain types of information are excluded from PIPEDA. Business contact information such as an employee's name, title, business address, telephone number or email addresses that is collected, used or disclosed solely for the purpose of communicating with that person in relation to their employment or profession.

Cybersecurity in healthcare threats continue to evolve and, in turn, so must cybersecurity solutions to combat these threats. In order to stay ahead of these threats, we must increase our situational awareness about what is happening and share more information about what is going on with our peers and colleagues. Healthcare organizations must continue to support cybersecurity professionals as they help to safeguard patient data. There is no better time than the present to increase cybersecurity defenses, while enhancing the capabilities and knowledge of staff.

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Cybersecurity in Healthcare Laws and Regulations The Health Insurance Portability and Accountability Act ( HIPAA) is a federal requirement in the U.S. which applies to covered entities and business associates. HIPAA consists of the HIPAA Privacy Rule, Security Rule and Breach Notification Rule.

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Privacy and Security have increasingly become a problem with the Electronic Health Record system. There have been reported "hackers" and breaches in security that jeopardize sensitive patient information. What are ways that we can protect our patients? Does moving to an electronic system create more problems with privacy than it protects?

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This week the material focused on the storytelling of the data we use. It is our duty to present the data in a way that is interesting in order to get the important information across to the audience. How can we get patients' attention in different settings to do preventative care? As provider we come across new data from published evidence based practice, updated guidelines, or new published information in a local level. We need to make the information easy to read and get the attention of our patients in order to promote the change in our own practice.

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Data use can be very taxing especially for those who find statistics and data collecting as "boring." The goal is to focus on how the data can be beneficial for the target audience. How can we make this "boring" data appealing to get the point across that we want? Explanatory data looks to pick through the information you have collected and make it appealing with a clean message. Get rid of clutter and big medical jargon. Make the information easy to read, interesting, and keep the language at the level of 5th and 6th graders. If the information isn't received the way you intended or with the significance you desire, reflect back to how the information was delivered.

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https://www.coursera.org/lecture/dataviz-design/what-is-exploratory-and-explanatory-analysis-QeFi3

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In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.

View Syllabus

Skills You'll Learn

Data Analysis, Tableau Software, Data Virtualization, Data Visualization (DataViz)

Reviews

5 stars

61.02%

4 stars

27.17%

3 stars

8.46%

2 stars

1.83%

1 star

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VL

Mar 10, 2018

The course uncovers the science of the design for data. It is certainly very helpful for my job where have to present a lot of data in different perspectives for senior management.

TN

May 19, 2019

The course is not only focusing on the Tableau project, but also discussing key notes that need applying to make visualization become more effective. That is great course! Thanks!

From the lesson

Design Best Practices and Exploratory Analysis

In this module, we revisit some of the concepts introduced from the previous module. You will be able to apply Gestalt Principles and leverage pre-attentive attributes in your visualizations. You will examine the role of accessibility and aesthetics play in your creations. Also, you will be able to define the ideas of exploratory and explanatory analysis and be able to normalize your data and identify outliers. Finally, you will be introduced to a challenging concept and construct a control chart to set you up to perform more advanced exploratory analysis.

Taught By

Govind Acharya

Principal Analyst

Hunter Whitney

Sr. Consultant, Author, Instructor

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What is Exploratory and Explanatory Analysis? - Design Best Practices and Exploratory Analysis | Coursera

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Video created by University of California, Davis for the course “Essential Design Principles for Tableau”. In this module, we revisit some of the concepts introduced from the previous module. You will be able to apply Gestalt Principles and ...

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Infographics were discussed in detail in how they can be used as a visual tool in healthcare. What are ways we can get patients' attention about significant diseases? How can we introduce a new treatment or procedure that may seem confusing to understand? How can we display the significance of new evidence behind these big changes in healthcare practice? Our job is to focus on the target audience and make the message simple.

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https://www.youtube.com/watch?v=RJmUulqUA-I&t=44s
This heart mapping vest video was created to get the interest of others by bringing excitement to the new technology through visualization via Medtronics.

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https://www.coursera.org/lecture/health-informatics-professional/data-visualization-part-1-hwt3r

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Data Visualization Part 1 - Informatics and the Foundation of Knowledge | Coursera

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Video created by University of Minnesota for the course “Interprofessional Healthcare Informatics ”. This module explores the contribution of healthcare informatics to the foundation of knowledge in healthcare. By the end of this module, learners ...

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The fact that data and data visualization are everywhere in today's world, makes me wonder how data visualization impacts medical applications and what's their role within patients' diagnoses? Undoubtedly, patients trust their wellness on healthcare professionals' criteria and decisions but what do doctors, nurses, practitioners, and other healthcare professionals base their judgment and decisions on?

The experience factor indeed drives medical decisions but when talking about complex medical diagnoses, healthcare professionals need to base their decisions and judgments on accurate information coming from different sources such as laboratory analyses made by experts (e.g., medical technologists) who are constantly involved in working with medical observations, samples, data, test results, researches, etc.

For professionals working in the health sector and treating hundreds of patients a year, it is key to trust reliable and accurate data, but how does data visualization impact the medical sector?

Key Takeaways

1. The Healthcare industry is one of the industries that generates the most data every year. In 2020, it was estimated that this sector created around 2,314 exabytes of data.

2. High-performance medical applications benefit from data visualization to visualize large datasets and support more accurate decision-making processes including diseases treatments and diagnoses.

3. Visualizations featured in MedTech and Healthcare applications demand the highest performance charting components.

Table of Contents

Why is Data Visualization Important in MedTech and Healthcare?

We've all seen at least one popular TV show or movie that showcases an example of a medical application that monitors a patient's e.g., heart rate, that looks like this:

Example of an Electrocardiogram (ECG)

So, even for non-medical readers, it becomes more intuitive to get the idea of a heartbeat visualization. But what would happen if you'd have to visualize the (sample) dataset instead? Let's take a look at a sample dataset of a heart rate contained in a spreadsheet:

The image above is a sample dataset containing hundreds of data points that at the bare eye, is tedious to understand even for medical professionals. In that case, how is data visualization used in healthcare? Data visualization helps in the process of transforming and visually displaying medical data points that simplify and synthesize the analysis of complex and large datasets into visualizations that are easier to process, visualize, and understand.

With that being said, data visualization and healthcare imaging offer another way how to understand data even for non-technical readers. Nowadays, medical applications that help users to track basic medical information are more common and can be found on people's smartphones and smartwatches.

So, it wouldn't be useful for users to visualize spreadsheets on their smart devices but rather translate complex data into simpler visualizations. After this concept, it is easier to understand why medical visualization plays a key role in the medical sector.

According to Statista, the amount of healthcare data generated in 2013 was 153 exabytes (where 1 exabyte equivalents to 1 billion gigabytes) and by the end of 2020, it had already increased to the colossal amount of 2,314 exabytes.

With the upward trend of the development of more medical monitoring applications for organizations and individuals, the amount of data that will need to be visualized, analyzed, and explored will indeed continue to grow rapidly in the upcoming years.

The continuous growth of medical data is a key driver for health organizations that are trying to provide a better and more personal patient-centric health service. As a consequence of this, data analytics, big data, and data visualization are becoming important subjects in healthcare that are helping to prevent and predict diseases.

The analysis of data represents a tremendous opportunity for health specialists to make data-driven decisions based on historical information that is later presented in state-of-the-art applications featuring the most advanced data visualization technology.

Preventive & Predictive Medical Visualization

While the future cannot be predicted, certain branches of data analytics in medical technology are present in the healthcare sector in order to convey different analyses that help professionals to work with past and future diagnoses. That is the case of descriptive analytics that takes a look into the performance of historical data and its behavior and relevance in the matter, e.g, in diagnoses.

The diagnostic analysis aims to understand why certain diseases occur and to explain their root causes whereas predictive analysis aims to forecast and anticipate future diagnoses and diseases which, therefore, acts as a method of prevention. Finally, the prescriptive analysis aims to test multiple scenarios to anticipate the outcomes before a final decision is made by a health professional.

The importance of data-related subjects such as data visualization is not only for health monitoring but also helps medical professionals to step ahead into possible future diseases and explain the triggers of past illnesses and harmful conditions.

Here's an example of a 3D volume rendering visualization of a skull that involves thousands of data points and is commonly used for studies in anatomy, traumatology, implants design, craniofacial reconstruction, or surgery analysis and planning, among other applications.
Example of a 3D Volume Rendering Skull for Medical Imaging & Visualization
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What is Medical Data Visualization?

As earlier mentioned, the volumes of data are steadily increasing in the healthcare sector and more medical professionals continue to experience difficulties when trying to make sense of patients' health information.

Within the different areas of data, data visualization specializes in transforming complex healthcare data and analyses into visual formats that help medical decision-making professionals to formulate diagnoses and treatments for patients in order to deliver effective medical care.

High-quality visualizations have been determined to be key in improving how health professionals perform decision-making processes and identify patients' health trends to understand future conditions ( predictive analysis) and diseases.

Technically, medical data visualization refers to increasing the efficiency and accuracy when reviewing and observing data for instance when visualizing organs, bone elements, or diseases processes. So, the process of medical or advanced data visualization helps to merge information from multiple sources that would otherwise stand alone and could not be related.

An excellent example of this was made by researchers Jonathan Wanderer, Jesse Ehrenfeld, and Soojin Park who stated:

"A visualization made for evaluating a kidney injury could merge multiple data from sources such as urine, urine electrolytes, serum creatinine, and blood pressure allowing doctors to quickly review and predict a change in the normal functions of the kidney."

Such visualization can be later used to understand and explore the disease, discuss the condition with other medical experts, and present the diagnoses to the patient's family.
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Data Analytics & Data Visualization Applications in Healthcare

Almost all the areas of healthcare and medicine can apply data analytics including data visualization. Today, modern medicine continuously uses data to provide a patient-centered approach and a more customized healthcare service. Moreover, data visualization in health IT is driving professionals to better decision-making processes.

Several areas of medicine have been improved and benefited from 3D models, e.g., x-rays, MRIs, or 3D volume renderings. High-performance visualizations are used within, if not all, the most demanding applications in healthcare, for instance, telesurgery, monitoring, surgical planning, or genomic analyses.

Molecular Analysis

In molecular analysis, Omics data refers to data sources from molecular fields where data is used with the aiming of understanding how diseases develop and how to improve the diagnoses of medical treatments.

Common visualizations used in this field are so-called N-body simulations that simulate the behavior of particles within environments, e.g., the human body system.
Example of N-body simulation
Genomics

Genomics refers to the study of genes and their functionalities which is key for researchers and medical experts when studying the prevention and cure of certain diseases. Visualizing data is key for observing and analyzing the correlation and trends that exist within large data sets that would otherwise be more complicated to analyze.

Medical Device Development

Data visualizations are commonly and widely adopted in medical devices for, e.g., cardiac rhythm monitoring, cardiovascular devices, diabetes monitoring, and prevention, etc.
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Charts & Graphs Used in Healthcare Visualization

Charts and graphs plot the data with respect to different axes creating specific forms or visualizations. As mentioned before, the amount of healthcare data is enormous and continuously growing thus demanding visualizations to be able to render a great number of data points at high-performance characteristics.

The requirements of the visualizations will be driven by the clinical needs but some of the characteristics of the highest performing visualizations would include:

Powerful data visualization libraries. There are several open-source and commercial libraries written in different languages including JavaScript, Python, or .NET. Depending on their nature, these libraries are designed for basic or advanced data visualization purposes.

Rendering performance. Not all libraries are meant to handle advanced medical and healthcare data sources. Open-source libraries experience this problem as they are limited in resources and performance. Commercial libraries are there to solve those issues and can render billions of data points in real-time without performance issues.

Resources optimization. Is the healthcare sector prepared to visualize all its data? Certainly, it is preparing for and GPU-accelerated libraries are using the PCs' power to deliver higher-quality visualizations at faster rendering and response times.

Here are 3 examples of advanced visualizations that can be later integrated into medical applications:
Interact
1. Medical Dashboard

Developed in JavaScript, this medical dashboard features real-time visualizations of four different channels. They process the data as it is received. The channels included are:

Electrocardiogram (ECG/EKG)

Non-invasive blood pressure measurement (NIBP)

Blood flow volume (BFV)

Blood oxygen saturation (Sp02)

2. 3D Volume Rendering of a Skeleton

This is a .NET medical visualization which samples a WPF 3D volume rendering of a human skeleton.

3. Electrocardiogram

Interact

This example of a JavaScript electrocardiogram featuring a line series in an XY chart. There are 960 signal points per second sent to the chart and its common application is in cardio and electrical activity monitoring.

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5 Companies Innovating in Healthcare & Medical Visualization

Medtronic is a world-leading diagnostic and health care company with products and services around the world. With operations in 150 countries, Medtronic products treat 70 health conditions and include cardiac devices, cranial and spine robotics, insulin pumps, surgical tools, patient monitoring systems, and more.

Within Medtronic's vast variety of healthcare solutions, they have developed the CardioInsight Workstation that is a dedicated workstation for ECG signals monitoring capable of rendering 3D cardiac maps.

The workstation works together with their product CardioInsight Mapping Vest which transmits the data to the CardioInsight Workstation and translates it into real-time high-performance data visualizations using LightningChart data visualization library.

Here's a practical case of the CardioInsight Workstation together with the CardioInsight Vest being used to analyze and treat a patient's arrhythmia. The case took place in one of the Northwell Health network hospitals.

2. MGC Diagnostics

Headquartered in Minnesota, US, MCG Diagnostics was founded in 1977 and today is one of the world leaders in the development of cardiorespiratory health solutions. They have a great scope of devices for the healthcare sector including devices for detection, classification, and management of cardiorespiratory diseases.

MCG Diagnostics is implementing high-performance data visualization solutions and features LightningChart data visualization charts and graphs in their Ultima™ CardiO2® gas exchange analysis system. This novelty device is an easy-to-use metabolic stress testing system with high-performance features including oxygen and carbon dioxide sensors to collect data from patients breathing, continuous data processing and analysis, and data visualization in real-time.

Source

3. BiosenseWebster

Biosense Webster specializes in the development of world-class medical devices for 3D navigation. They are innovators in the field of electrophysiology and have developed their novel CARTO™ 3D mapping technology used for signal monitoring and diagnoses that are improving the lives of patients with cardiac arrhythmias condition.

Biosense Webster relies on implementing powerful data visualization libraries capable to process signal data in real-time that helps healthcare experts to analyze a patient's conditions and provide a better diagnosis. Some of the visualizations used in the CARTO™ 3D mapping implement LightningChart .NET 3D volumetric rendering.
Source

Here's a detailed demonstration of a real-time ablation medical procedure made with the Carto system where abnormal tissues are removed.

4. Digicare Biomedical Technology

Is a family-owned and innovative company that provides devices solutions for medical monitoring and measurement. Digicare Biomedical Technology has implemented LightningChart Data Visualization Solutions in their novel telemetry monitoring system CentralVue CV10 that features different line series and XY charts that support millions of data points processing in real-time.
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The CentralVue CV10 is a medical multi-channel monitoring system that supports up to 32 different monitors with features including patient management and remote controlling.

5. Cerebra Sleep Scoring

As world-class innovators in the field of sleep research, Cerebra has studied, researched, and developed different solutions to improve the way how patients develop a better quality of life by enhancing their sleeping conditions.

Their Cerebra Sleep System directly analyzes the Electroencephalogram signal to observe the brain activity of patients and develop effective diagnoses that can improve the physical, mental, and emotional health of patients.

Within their system, Cerebra implements LightningChart XY and line series data visualizations for different monitoring of patients' sleeping signals coming from brain activity.

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Why Does Performance Matter in MedTech and Healthcare Data Visualization Applications?

When vendors in the MedTech and healthcare industry are evaluating charting products for processing their data, analysis, and visualizations, their top priority is high-performing charting components or data visualization solutions .

In the medicine field, data visualization is part of a bigger process where data needs to be processed at top performance with high accuracy avoiding downgrading techniques like downsampling.

End-users are health professionals who are experts in analyzing the information and translating it to their patients. Health professionals need to rely on fast and accurate tools provided by the best-performing medical devices that feature high-performance components.

The importance of high-performance in MedTech and Healthcare fits within the ability to transform millions and even billions of data points into high-performing visualizations that support the healthcare experts' decision-making processes.

High-performance data visualization is therefore crucial for MedTech applications to present data either on desktop, web, or mobile devices. Charts, graphs, and dashboards need to be able to handle millions of data points in real-time, to enable doctors and research professionals to have complete visibility and control over their instruments and MedTech applications to provide better healthcare services.

LightningChart ® Data Visualization Components for MedTech & Healthcare Application Development

Some of LightningChart's key points that support high-performance and large datasets visualization are:

MedTech cross-platform application compatibility. LightningChart data visualization library supports app development for desktop, mobile, and web applications.

Line series are commonly used in MedTech applications. LightningChart line series support visualizing up to 500 million data points.

Volumetric rendering, XY charts, and 200+ charts available. LightningChart provides the most extensive library of high-performance, interactive, and modern-looking visualizations for both .NET and JavaScript application development.

Guarantees the best performance for MedTech apps. Integrating LightningChart library to your MedTech application guarantees only the fastest response times, best hardware acceleration, and GPU-accelerated visualizations. E.g., LightningChart line charts are up to 516,600 times more efficient when rendering healthcare data.

When it comes to visualizing healthcare data, LightningChart charting components support the world's fastest and largest rendering technologies that leverage the way how healthcare experts and end-users visualize and understand the data allowing more accurate diagnoses and decision-making processes.

LightningChart® Data Visualization Solutions are advanced data visualization libraries for .NET and JavaScript that process hundreds of charts and dashboards built to handle billions of data points in real-time that support MedTech and Healthcare applications manufacturers to provide the best performing applications for end-users.

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Data Visualization in Medical & Healthcare Applications

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The fact that data and data visualization are everywhere in today's world, makes me wonder how data visualization impacts medical applications and what's their role within patients' diagnoses? Undoubtedly, patients trust their wellness on healthcare professionals' criteria and decisions but what do doctors, nurses, practitioners, and other healthcare professionals base their judgment and decisions on?

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https://study.com/academy/lesson/ethical-considerations-related-to-patient-centered-it.html

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Infographics are a great way to get the attention of audience via graphics, colors, and other visual tools. As providers we need to take responsibility for staying current in our practice with changes to guidelines, new published data, and new treatments. When looking at published data in healthcare, we know that information isn't technically current. When finally submitting as Evidence Based Practice guidelines, the data is already 10-15 years old.

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Although the implementation of the Electronic Health Record System can be beneficial in several ways, it also creates a problem with cybersecurity. Patient health information can be accessed by people who shouldn't be able to view them such as other healthcare workers not involved in the care as well as the potential of data breaching. Now that the information is electronically placed in a portal system there are potential for cyber hacking and exposure of health records. It is our duty to secure patient information and implement a security system to protect that.

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When looking at healthcare informatics, all of the basic ethical principles carry on but now on a larger scale via the Electronic Health Record System. We now have a duty to protect our patients' information electronically. Are we ensuring patient privacy by closing their chart or logging out of the computer when we step away? Are we giving our password away to another user to access something because it is easier to do that than call the IT worker? Protecting patient information should be our number one goal. Are we taking the extra step to ensure we are charting on the correct patient and submitting the correct information in the portal?