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- General Principles of Informatic Ethics is something that we deal with in the healthcare on a daily. An example was of principle of access where templates are used and inaccurate information is passed down such as the pelvic exam done but it wasn't. We must slow down and be careful as I have seen this happen in notes from providers.
- The interview with other providers and their thoughts on 23 and me. I agree that there is no context and they should have a genetics counselor to guide and provide information on results of the testing.
- 23 and me can provide information on genetics but not diagnose, and this is where consumers must be aware of.

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- Data visualization helps people understand data with a visual presentation such as pie charts or graph charts.
- The goal is to use data visualization as data storytelling.
- Analysis of data should be exploratory vs explanatory.
- Infographics are almost like a brochure of information on many topics. Examples in healthcare can be stroke and diabetes. Pinterest has a lot of examples, and I have seen many of these in clinics and other healthcare settings and fairs.
- An example was the video of the MRI of the brain is a tool you can use as data visualization, but also you can use information in a presentation in storytelling by use of pictures, pie charts, spider/radar charts, and graphs to show trends of data.

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- It was mentioned that PICO is commonly used in evidence-based research. I have learned about this being used in my BSN courses.
- Excel is a standard tool used for any research, and many people know how to use it.
- I agreed with Sharon Kurt, that data in conjunction with storytelling makes it more memorable
- Data given with no direction or visualization can be tedious. To convince or "sell ideas" to people, data storytelling would be the best tool used. It can provide context, meaning, and clarity while engaging the audience with storytelling.

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Using Storytelling to Effectively Communicate Data Tutorial | Aims Of Data Visualization

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PICO is one tool to help with data analytics commonly used for evidence-based research.

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- HIPAA is a big deal and is about privacy.
- Data and information can be lost through healthcare breaches. I find it scary that this is still a big problem such as when Anthem, a large company in healthcare had many information and records breached. The fact that our information is being sold in the black market terrifies me because this is OUR information.
- I feel that patient portal are great for both providers and patients. As a provider and a patient, we can have a better understanding of their care and we can better communicate with patients. As a patient, I found it convenient in communicating, make appointments, and check my medications and lab results.

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Electronic Health Records: Privacy and Security

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Helpful for both providers and patients. Great for understanding care and communicating both ways.

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Patient Portal – How to Send a Direct Message to Your Provider?

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Healthcare Security Breaches | Intel Business

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What is HIPAA? [HIPAA + Violation Penalties Explained]

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- Too often, we are quick to override alerts in EHRS, I have seen it myself in the healthcare setting
- Alerts may indicate drug-to-drug interaction, allergies, dosing, etc.
- Dr. Watcher writes a book about errors in EHR.
- An example given was a 16-year-old boy who was given 38.5 tablets of Septra when it was supposed to be 5mg/kg. Orders were placed incorrectly and even alerts overrode the medications. This is a serious concern as we have fewer airplane mechanical errors than medication errors with EHRs.
- Video on airplane architecture and healthcare similarities. They both use a team from different disciplines to create better technology. The technology created is not created to replace us, but to better execute our jobs in improving safety and efficiency.
- I also agree with Dr. Robert Watcher's video about how technology has taken away human interaction and empathy. We tend to be on our computers looking for results, ordering medications, and just reading over information instead of interacting with our patients with full eye contact. Technology can be useful but I feel that the trust and connection you make with your patient should also be a priority and never be taken away by technology.
- PHRs are managed and used by patients. It is not owned by institutions or hospitals. It contains our name, history, lab results, and medications. It helps us organize and have ease of access to our health care records.
- Patient portals are being used by many hospitals where patients can access their information, request refills on medications, and most importantly, message and communicate with providers. It makes it easy for patients when they have limited time or challenges in making an appointment or communicating with their provider.

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Electronic checklists can help keep things organized and make things efficient for us.

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Dr. Robert Watcher was skeptical about EHRs being helpful and wrote a book about it.

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- Gamification can be used for research and to help us live a better life.
- Different examples include Sea Hero Quest to help gather information on Dementia, Build a Healthy Plate for diabetes education, Quit Start to quit smoking, and Zombie Run to help make running entertaining.
- I didn't realize that they used games and applications to educate and research on certain conditions, which I find very fascinating.
- I have used the Zombie Run application on my phone a few years ago and found that it made running entertaining.
- Wearables are the future as we have been using watches to monitor our fitness and health such as ECGs and counting steps.

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A game to gather information for Alzheimers research

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Sea Hero Quest Virtual Reality | Game Trailer

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Gamification can be used to help us live a better and healthier life

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- Telehealth is on the rise as it provides remote healthcare through video conferencing through apps, patient portals, call centers, and through remote monitoring by devices.
- There are different ways to provide health through tele health such as providing weight and BP from home when monitoring for CHF and remote call centers to take after hour clinic phone calls just to name a few.
- Pros is it is convenient for busy people improving access to care and allows one to have care w/i the privacy of their homes.
- Cons can be overutilization and difficulty doing physical exams to allow for proper diagnosis.
- Telemedicine uses technology to allow for communication about health between providers and patient
- An example of telemedicine is using an apple watch to monitor for irregular heart beats for a patient that has a hx of afib. The provider is able to look at trends.
- Health Informatics is global. Examples include: Canadian Health authorities requiring EHR systems to be standardized, China making HIT a top priority in its national health reform program, and Malaysia planning to have a LHR- Lifetime Health Record.
- International Organizations include eHealth, WHO, and Global Health Technology Coalition

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Uses technology to allow providers to interact with each other and with patients

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Take Away:
- HIE is an electronic tool that improves care by reviewing patient information in a timely manner to focus on quality patient care instead of spending hours faxing, emailing, and mailing information.
- HIE is timely, efficient, and effective information sharing where providers have access in a matter of minutes vs hours.
- HIE include data such as lab values, allergies, and medication list
- Datasets include many sources such as registries for diseases, EHRs, and claims data just to name a few.
- There are two types of data: Private and public
- There are two levels: Metadata and Ontologies

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Two levels of datasets used in documentation:
1. Metadata (gives information about other data)
2. Ontologies (set of concepts and categories such as ICD)
Sources:
- EHRs
- Data that is reported such as immunizations
- Claims
- Health Surveys
- Disease Registries

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A value to providing efficient patient care by having instant access to data such as labs, diagnostic tests, medication list from other visits to a health care clinic/facility.

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Understanding the State's Health Information Exchange (HIE)

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As an FNP we can prescribe medications such as antibiotics, pain medications, etc. with the supervision of a physician. E-prescribing is what is used in many facilities where it is a way to send prescriptions directly to the pharmacy electronically with the benefit of decreasing errors and the patient receiving medications efficiently, thus improving quality of care. This article is based on prescription errors before and after the introduction of electronic charts.
- Goal: Identify risks and benefits of electronic prescription to decrease harm from prescription errors.
- Study: "hand-written" prescriptions were studied where pharmacists logged errors and physicians assessed the potential for harm. This was compared with a new inpatient system involving e-prescription.
- Results: 183 Hand-written prescription errors vs 178 e-prescription errors. Errors included: Wrong dose, drug, route, incorrect instructions, and prescribing drugs with allergy interactions. E-prescriptions over-ruled hand-written prescriptions with fewer errors and less severe errors.

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Introduction

The three most frequently occurring types of medication error reported to the National Patient Safety Agency's National Reporting and Learning System are wrong dose, omitted medicine and wrong medicine. Mistakes occur at all stages of prescribing, from the decision to use a medicine in the treatment of an individual to writing it correctly and legibly - including calculating or selecting the correct dose or dosage form. At the decision stage, knowledge of the patient's history of medicines use and any previous adverse drug event must be taken into account; if it is not then a prescribing error has been made.

Strategies for reducing errors include organisational interventions such as ward pharmacy services, and process changes such as the use of information technology in the ordering of medicines.

An electronic prescribing system (e-prescribing) solves problems with illegibility and minimises mistakes in transcribing and processing (preparing and dispensing the ordered medicine). In addition, many hospital e-prescribing systems provide computerised decision support, with links direct from the prescribing screen to, for example, laboratory results, allergy data and prompts or alerts for interactions and duplicate medicines. However, new types of error may be introduced, for example, through careless selection from a drop-down menu of the wrong drug or the wrong dose.

Having recently implemented inpatient e-prescribing and administration recording, we were interested in comparing features of this system with traditional handwritten prescriptions (HWPs) on paper drug charts. We report here on the findings from pharmacists' intervention data, where prescribing errors were judged for their potential for patient harm had they not been intercepted.

Methods

Ethical approval was obtained from the local research ethics committee.

Selection criteria/participants

The study was carried out in a 480-bed teaching hospital undergoing full implementation of e-prescribing. All outpatient and take-home prescriptions for discharged inpatients were already computer-generated using an existing system. Over a period of several months all wards were being changed to e-prescribing from traditional handwritten drug charts.

The e-prescribing system includes the hospital formulary as a menu for drop-down selection and a separate list of non-formulary medicines (both of which have inbuilt decision support). The system also features link buttons for accessing patient demographics, laboratory data, radiology, communication notes from other users and the British National Formulary, the BNF for Children and all hospital procedures, policies and guidelines via the intranet. At all times during prescribing, the patient's allergies and weight are on the screen (see Figure 1).

Figure 1: Screen shot of e-prescribing interface

Alerts "pop up" as a clinician enters or checks medicines to which the patient is known to be allergic, that may interact with other prescribed medicines or are duplicates (see Figure 2).

Figure 2: Screen shot of e-prescribing alert

These alerts have to be "answered" from a menu on screen before the prescription entry can be made.

We conducted a before-and-after study on two adult surgical wards - the first to move to inpatient electronic prescribing. The timetable for implementation was set several months before the overnight changeover on these wards from HWPs. Each ward has 28 beds for patients admitted for surgery requiring at least an overnight stay. All prescriptions were checked and, where necessary, the prescriber contacted for clarification or alteration as per the usual ward pharmacy service.

Data collection

Pharmacists recorded all consecutive interventions (defined as any communication between pharmacist and clinician, verbal or written, with the intention of influencing prescribing) for four months immediately before e-prescribing and then on the same wards after full implementation. The periods of study were of the same four-month duration before and after implementation. There were no other changes made to the ward that might be expected to influence prescribing. A settling-in period of three months was allowed between implementation and the "after" phase of the study.

Interventions were coded by type. Certain cases were not considered prescribing errors for the purposes of this study, for example, using brand name or non-formulary prescribing. This is in line with previously published data on errors. Errors intercepted were categorised as omissions, wrong drug, wrong dose, wrong route or other incorrect instruction (after Dean et al). We included a further category: unauthorised prescriber.

Where there was an unintentional omission of a regular medicine which should have been continued, we noted if this occurred at admission to hospital (where the drug history would have had to have been checked).

Prescribing errors involving wrong drug included prescriptions for a medicine: to which the patient has a documented allergy or intolerance (but not where it develops later); that interacts with other prescribed medicines; or is an inadvertent duplication of same or similar medicines.

We recruited three senior pharmacists and three consultant doctors to assess the severity of the prescribing errors, as recorded and described by the intervening pharmacists. They scored all the error cases individually, in terms of their potential for harm, on a visual analogue scale from 0 to 10 (where 0 represented an error with no potential effect on the patient and 10 an incident that would be likely to result in death). The mean score for each intervention was the harm score. This is a validated scoring system used in previous studies of medication errors.

Severity categories were assigned according to the mean harm score:

Low - harm score 0-3

Moderate - harm score 4-7

Serious - harm score 8-10

All coded interventions were entered into an SPSS database for analysis, comparing those collated before with those after implementation of inpatient e-prescribing. A P-value of less than 0.05 was set as the significance level.

Results

Pharmacists intervened on 251 HWPs and 248 electronic prescriptions. From these interventions, as defined in the method, we found and assessed 183 and 178 prescribing errors, respectively.

The most common type of error pharmacists identified was omission (regular medicine unintentionally missed from a rewritten or transcribed prescription); this accounted for 41.5% of HWP errors and 37% of e-prescription errors. Such errors occurred particularly at admission, with the admitting doctor inadvertently failing to prescribe regular medicines from the drug history (43 of the 72 HWP omissions and 28 of the 66 e-prescribing omissions).

The harm score average before e-prescribing was 2.46 and after was 2.18 ( t-test for equality of means, 95% confidence interval 0.07-0.49; P=0.009), representing a reduction in severity of 11%. The stages of the prescribing process where each error was intercepted are shown in Box 1, along with the harm category assigned.

Box 1: Type of error identified and harm category assigned

We judged as having serious potential for harm one error intercepted from an HWP and none from e-prescribing. The HWP with a serious potential for harm was for a patient known to be allergic to penicillin who was prescribed co-amoxiclav as a new discharge medicine.

Fifteen (8%) of the HWP errors, and four (2%) of the e-prescribing errors, were judged to have potential for moderate harm. Of the four e-prescribing errors, two involved the prescriber over-riding inbuilt alerts:

Co-codamol prescribed together with paracetamol

Morphine prescribed for an inpatient who was allergic to the drug

The two other moderate e-prescribing errors are considered likely to occur with either system:

Pharmacist asked doctor to review prescribing of diclofenac in view of deteriorating kidney function (as indicated by assessment of creatinine clearance)

Diazepam 10mg/2ml emulsion injection prescribed via intramuscular route (wrong route for this formulation)

In both phases of the study, most errors were scored as low harm. When those that occurred during e-prescribing were examined we found that, on more than one occasion, a lack of familiarity with the computerised entry system may have contributed to these errors. For example:

Error in selecting aspirin from menu as enteric-coated instead of the intended dispersible formulation

Potassium 20mmol in glucose 5%, ordered as glucose with additive detailed in the comments section (the prescriber should have selected a pre-mixed bag from the system)

Prescription for cefuroxime for surgical prophylaxis prescribed as 750-1,500mg three times a day (eight hourly) for one day instead of three stat doses sequentially (1,500mg, 750mg and 750mg) which can be entered as an order set

Discussion

We have shown benefits of e-prescribing over HWPs in reducing the severity of potential harm to patients from prescribing errors.

There is some difficulty comparing our results with those from other researchers because of the differences in systems used, especially outside the UK. Many researchers refer to "computerised physician order entry systems" (CPOEs) which may or may not have a clinical decision support system inbuilt. This is an inherent limitation of our study.

In addition, our patient population was limited to surgical wards, where there is relatively less prescribing outside protocol, for example, for antibiotic prophylaxis. We did include particular analysis of medicines stopped or continued from the patients' drug histories at admission; some studies may not have counted these as errors.

Our denominator was not the number of prescriptions seen but a period equal in duration before and after implementation of e-prescribing. Our rates of errors are therefore over time and can only usefully be compared before and after at our hospital rather than with any degree of certainty against rates elsewhere.

In another UK study similar to ours, the prescribing error rates on a 36-bed orthopaedic ward were significantly reduced after introducing e-prescribing (from 7.4% to 4.7%). Looking at the incidence of all medication errors at a hospital in Belgium where paper and e-prescribing coexist, a lower incidence (3.4%) was measured in e-prescribing compared with paper-based prescriptions (27%).

Dean and colleagues found the frequency of prescribing errors fell from 3.8% before to 2.0% after the introduction of their "closed loop e-prescribing and dispensing system", although no difference was found in harm scores before and after. Their average harm score means were higher at 4.2 and 4.6, compared with our study findings of 2.5 and 2.2. Differences in data collection methods and the e-prescribing systems used may account for this.

Another UK study conducted in a renal unit, involving nearly 90,000 medication orders placed electronically, showed that 58% were "disallowed" by the computer system because they were for allergies and serious interactions that might otherwise have resulted in an adverse event. Alerts have also been shown to reduce likelihood of dosing errors for patients with renal insufficiency and in prescribing for thromboprophylaxis by our team, previously reported.

In our study, many of the HWP errors were due to medicines prescribed for patients with known allergy and judged as having potential for moderate harm. The one scored as serious was an allergy conflict. It may be that these errors are reduced because prescribers are better alerted to allergies when using e-prescribing compared with paper prescriptions.

In a prospective cohort study conducted in a UK intensive care unit over a 70-week period, prescribing errors were significantly fewer for e-prescribing than for handwritten orders (4.8% and 6.9%, respectively; P<0.04). However, there were three e-prescribing errors that, had the medicines been administered, would have led to permanent harm or death. Intensive care was not included in our study so direct comparisons cannot be made but, examining the details, we believe that these serious errors are unlikely to occur with our local e-prescribing system because of the alerts and inbuilt decision support.

Many of our prescribing errors were omissions found by pharmacists when undertaking medicines reconciliation soon after patient admission; overall these accounted for nearly 20% of all intercepted errors. This is a common finding in keeping with those from other studies and gives support to pharmacists continued vigilance in the medicines reconciliation process. There was a reduction in omission errors with e-prescribing; greater clarity of the documentation of drug histories on the computer screen compared with HWPs may be a factor.

In one published study, 61% of all patients were found to have one or more of their regular medicines not entered on the e-prescribing system. A later study showed that omissions from drug histories accounted for 62% of all medication errors at a UK hospital's acute medical assessment unit. The authors judged 20% of these to be moderate errors, and 0.4% major errors. No omissions in our study were judged as serious.

An investigation into the causes of prescribing errors in a London teaching hospital showed that a lapse in attention or failure to apply relevant "rules" (eg, how to reduce a dose in renal failure) were common underlying causes. Having electronic links to such rules might be expected to reduce errors caused by the latter.

Prescribing rules were also referred to in the EQUIP study, along with miscommunication on the part of a third party, and deficiency of (often complex) knowledge. An editorial in The Lancet commenting on the EQUIP study findings suggested that the variety of UK hospital HWP charts contribute to error rates and recommended standardisation as a method to minimise errors. There was, however, no reference to electronic prescription forms.

When an error in prescribing (including an omission) is identified by pharmacists, nurses or other healthcare colleagues, the prescriber is contacted and, in some cases, the details and outcome of the intervention are documented. These documented data do not represent total numbers of prescribing errors - some will, inevitably, be missed by everyone and many will not be recorded. Nor is the total number of medication errors picked up, since only the prescribing, and to a certain extent transcribing and monitoring, is assessed but not mistakes made in the other areas. Delays in prescribing might be expected with a new system. This type of prescribing "error" was not adequately assessed using our study methodology. However, intervention monitoring, in addition to providing a sample for investigating prescribing errors as described here, can guide and influence training and inform organisations of near misses and ways in which risk to patients can be minimised.

Conclusions

We have shown that a fully computerised system for prescribing inpatient medicines decreases the potential for harm to patients. Although errors are still made and intercepted by pharmacists, their severity is reduced significantly. Our locally adopted electronic system contributes to the goal of error-free prescribing, reducing potential for harm compared with traditional handwritten methods. However, like other researchers (despite comparisons being limited by differences in e-prescribing systems) we found errors are not eliminated at every stage of the prescribing process and different errors, albeit with less potential for harm, are introduced. During the study period, and after the introduction of electronic prescribing, locally there were no errors identified that had potential for serious harm.

Acknowledgements

Thanks to Nick Cooley, pharmacist, Mark Bower, consultant medical oncologist, Deirdre Richardson, pharmacist, and Chris Chung, pharmacist, all at Chelsea and Westminster Hospital NHS Foundation Trust. Thanks also to Neil McCall Peat, pharmacist and data collation and electronic prescribing specialist, formerly at Chelsea and Westminster Hospital NHS Foundation Trust.

Declaration of interests

The authors declare that they have no competing interests.

About the authors

Vanessa Marvin is deputy chief pharmacist for clinical services, India Hardy was, at the time of writing, lead pharmacist for electronic prescribing, Richard Morgan is a consultant physician, Deirdre Linnard is head of pharmacy and David Asboe is a consultant physician, all at Chelsea and Westminster Hospital NHS Foundation Trust.

Email: vanessa.marvin@chelwest.nhs.uk

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Prescribing errors before and after introduction of electronic charts - The Pharmaceutical Journal

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Mistakes occur at all stages of prescribing, from the decision to use a medicine to writing it correctly and legibly - including calculating or selecting the correct dose or dosage form. At the decision stage, knowledge of the patient's history of medicines use and any previous adverse drug events must be taken into account; if it is not then a prescribing error has been made.

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- Learned the difference between EMR and EHR.
- EMR deals with a specific institution and not outside organizations
- EHR contains data from different specialties where patients are able to access information.
- EHR benefits cost, safety, and improved patient education and care.
- CDS is used on a daily to improve patient care
- CDS examples is such as drug-drug allergies that pops up during medication administration or when ordering medications.

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- Clinical Decision Support (CDS) uses technology to help provide patient care in a safe manner
- Components include:
1. Information recipients  2. Information delivered 3. Information delivery formats
- Example of CDS uses daily: GPS
- Example of CDS used in healthcare: Drug warnings
- Different types: Drug-drug interactions, alerts, document templates, dose range checking, etc.
- Examples of the Value of CDS:
- Quality, safety, cost, documentation, and communication
- Challenges:
- Speed, usability/workflow, avoiding over-alerting, cultural change, management backing, clinical leadership backing

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- Meaningful use is a part of CMS incentive program
- There are 4 buckets of Meaningful Use: 1. We purchase a certified EHR from a vendor certified by CMS 2. Capture data 3. Move data and 4. Report data
- Meaningful Use Stage 2 is the most common stage and has ten objectives
- The objectives and Stage 2 are utilized by providers with the help of the healthcare team such as medication reconciliation, using clinical decision support, and patient-specific education just to name a few. These objectives help improve quality of care and decrease errors. It also helps provide easy access for providers and patients to communicate and understand the care and interventions being provided.

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This week I learned about the DIKW hierarchy which stands for Data, Information, Knowledge, and Wisdom. this model represents less value to increasing value which is Wisdom. Data is basic raw values that have no meaning. The past include data, information, and knowledge, where "wisdom," is the future.

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This article is about setting a standard set of terminologies for prescription adherence using EHR and prescription databases. There has been a lack of consistency with terminology that affects medication adherence and persistence. The use of standardized terminology can improve medication adherence and quality of care for the patient.

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Med Care. Author manuscript; available in PMC 2014 Aug 1.

Published in final edited form as:

Marsha A. Raebel, PharmD, 1, ² Julie Schmittdiel, PhD, ³ Andrew J. Karter, PhD, ³ Jennifer L. Konieczny, PharmD, ⁴ and John F. Steiner, MD, MPH 1, ⁵

Abstract

Objective

To propose a unifying set of definitions for prescription adherence research utilizing electronic health record prescribing databases, prescription dispensing databases, and pharmacy claims databases and to provide a conceptual framework to operationalize these definitions consistently across studies.

Methods

We reviewed recent literature to identify definitions in electronic database studies of prescription-filling patterns for chronic oral medications. We then develop a conceptual model and propose standardized terminology and definitions to describe prescription-filling behavior from electronic databases.

Results

The conceptual model we propose defines two separate constructs: medication adherence and persistence. We define primary and secondary adherence as distinct sub-types of adherence. Metrics for estimating secondary adherence are discussed and critiqued, including a newer metric (New Prescription Medication Gap measure) that enables estimation of both primary and secondary adherence.

Discussion

Terminology currently used in prescription adherence research employing electronic databases lacks consistency. We propose a clear, consistent, broadly applicable conceptual model and terminology for such studies. The model and definitions facilitate research utilizing electronic medication prescribing, dispensing, and/or claims databases and encompasses the entire continuum of prescription-filling behavior.

Conclusion

Employing conceptually clear and consistent terminology to define medication adherence and persistence will facilitate future comparative effectiveness research and meta-analytic studies that utilize electronic prescription and dispensing records.

Keywords: medication adherence, medication persistence, medication discontinuation, refill compliance, refill persistence, administrative, database, electronic health record, computerized medical record systems

BACKGROUND

The construct of medication adherence comprises a set of inter-related health behaviors.1 One of these behaviors, the act of filling a medication prescription, can be estimated objectively using electronic databases such as electronic insurance claims or pharmacy dispensing databases. Numerous studies have assessed patterns of prescription refills among individuals who obtain at least one fill of a medication. 2-11 Over the last several years, advances in electronic prescribing and medication order entry within the electronic health record (EHR) have expanded our ability to assess whether or not patients obtain their initial prescriptions, another of the health behaviors within the adherence cluster.12-17 Although EHR prescribing databases vary in whether or not they contain information on dispensed prescriptions, their use can enhance widely available pharmacy dispensing and pharmacy insurance claims database to better describe the sequence of behaviors that are necessary to achieve desirable treatment outcomes.

The addition of this new source of information about prescription-filling emphasizes the long-recognized lack of uniform terminology and precise definitions to describe prescription-filling behavior.18,19 Organizations such as the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the World Health Organization (WHO) have put forward definitions of adherence and persistence; however these definitions did not encompass the entire range of data sources for adherence. Further, existing definitions do not define terms consistently, do not address the issues of medications that are prescribed but not dispensed, and do not address behaviors such as medication discontinuation.20,21 Finally, adherence-related publications sometimes neither define their terms carefully nor explain their choice of metrics, leaving readers to make assumptions about why metrics were chosen and how they were calculated.

Applying a uniform conceptual framework to electronic database studies of medication adherence and employing standardized terminology and definitions within that framework will enhance both rigor and generalizability of medication adherence research, as well as the ability of researchers to formally compare the findings of studies in systematic reviews. In this paper we propose a unifying set of definitions for use when studying prescription-filling for chronic oral medications in EHR, in pharmacy insurance claims databases, and in pharmacy dispensing databases and provide a framework to operationalize these definitions consistently across studies.

METHODS

Literature Review of Chronic Oral Medication Adherence Definitions in Electronic Database Studies

The purpose of this literature review was to inform development of the conceptual model by identifying definitions and metrics for terms such as medication adherence, persistence, and discontinuation used in published studies based on electronic databases, and the rationale provided by the authors of these studies for selecting definitions and measurement tools. To accomplish this, two authors of the current paper (JLK and MAR) searched published literature to identify EHR, pharmacy dispensing, and pharmacy insurance claims-based studies where medication adherence, persistence, and/or discontinuation was stated as a primary outcome. Although the purpose of this review was narrowly focused and a comprehensive literature review was neither intended nor undertaken, the authors utilized search techniques from the literature on systematic reviews. Retrospective observational studies, randomized controlled trials, and non-randomized comparative studies were included. The National Library of Medicine's Medical Subject Headings (MeSH) keyword nomenclature developed for MEDLINE ® and adapted for use in other databases was employed. The search was limited to studies published in English from January 1, 2000 through December 15, 2011, and to articles indexed in PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL®), Google Scholar, or the Web of Science. MeSH terms applied during the preliminary search were revised and refined based on expert input from a medical librarian and review of the MeSH terms identified from relevant publication titles retrieved in the preliminary search.

In the medical literature, the word adherence is applied to a broader variety of behaviors and regulatory topics than the focus of this work. Thus, studies that focused on adherence to or compliance with lifestyle, guidelines, exercise, diet, preventative screenings or follow-up, dental screenings/procedures, radiation/imaging, hospitalization/surgery, quality of care recommendations, medication reconciliation, drug administration/efficacy/adverse effects, device use, medical visits recommendations, isolation or hand washing precautions, cognitive/behavioral therapies, vaccination/immunization, and Health Insurance Portability and Accountability Act (HIPAA) regulations were excluded. Moreover, we excluded studies that used the degree of risk factor control as the indicator of adherence, rather than directly measuring medication use or non-use.

The title of each citation retrieved was reviewed and the abstract retrieved if the title indicated adherence or persistence to, or discontinuation of, chronic oral medications was the focus. Abstracts of articles dealing with validation of adherence measures used in electronic database studies were also included. Potentially relevant citations were imported into an electronic database. Abstracts of these citations were reviewed and the full texts of relevant articles were retrieved and read by the two individuals that conducted the literature search (MAR and JLK). As articles were read, the definitions and terminology used for adherence, persistence, and/or discontinuation in the articles were extracted and cataloged.

This literature scan retrieved 2484 articles, 315 (13%) of which utilized electronic data sources that included as the primary outcome chronic oral medication adherence or that evaluated adherence metrics. Studying the definitions and terminology we extracted from these publications confirmed our subjective impression of variation, inconsistency, and confusion in the terminology used. It also demonstrated that terms were used imprecisely and interchangeably to refer to different constructs in different papers. This background work reinforced the fact that a conceptual model of and uniform definitions for the adherence continuum were lacking. It also informed the standardized terminology and definitions we developed ( Tables 1 and ​and2 2 ).

TABLE 1

Terminology for
Medication Adherence
or Persistence Construct

Recommended
Foundational Definition

Components of Definition
Requiring Study-Specific
Decisions to Operationalize

Examples of Other
Terms Previously
Applied to the Same
Construct *

Adherence

Primary Adherence6,15

A new prescription was
dispensed ("filled" or sold)
within a defined number of
days after the medication was
ordered

Definition of "new prescription:"
How far to look back to determine if
the medication has been previously
dispensed; 12 or 24 months
commonly used

Definition of "defined number of
days:" The prescription must be
dispensed within "x" days after the
order was written, with "x"
commonly60 or 30 days 6,13,17

First-fill adherence13
Adoption32
Initiation33

Primary Non-adherence15-
17

Failure to have a new
prescription dispensed (did
not pick up the first
prescription) within a defined
number of days after the
medication was ordered

Definition of new prescription(see
Primary Adherence)

Definition of "defined number of
days:" The prescription must not be
dispensed within "x" days after
order was written, with "x"
commonly 60 or 30 days6,13,17

Dispensation delay34

Secondary Adherence6

Adherence measured among
patients with Primary
Adherence) and who have the
prescription refilled within a
defined number of days
following the end of the days'
supply of the first dispensing.
Secondary adherence is
usually measured over 6 or 12
months or longer †

Ongoing dispensing/use17
Implementation31

Adequate Secondary
Adherence

Secondary adherence with
either an overall a) gap in
days of medication possession
not exceeding 20% of the
days between the date of
initial dispensing and the date
of the end of the measurement
period (gap measures) or b)
number of days of medication
possession of no less than
80%of the days between the
date of initial dispensing and
date of the end of the
measurement period
(possession measures)

By convention - only rarely with
supporting evidence - the following
cut-points have commonly been
used:
Gap:
CMG < 20%
NPMG < 20%
Possession:
MPR >= 80%
PDC >= 80%

Adequate adherence35
Adherence33
Ongoing adherence6
Compliance

Inadequate Secondary
Adherence

Secondary adherence with
either an overall a) gap in
days of medication possession
exceeding 20% of the days
between the date of initial
dispensing and the date of the
end of the measurement
period or b) number of days of
medication possession of less
than 80% of the days between
the date of initial dispensing
and the date of the end of the
measurement period

By convention - only rarely with
supporting evidence - the following
cut-points have been used:
CMG > 20% to < 60% or upper cut-
point can be as high as 100%
NPMG > 20% to < 60% or upper
cut-point can be as high as < 100%
MPR > 40% to < 80% or lower cut-
point can be as low as 0%)
PDC > 40% to < 80% or lower cut-
point can be as low as 0%)
Sometimes further subcategorized
into MPR or PDC ≥ 60 - 80%, ≥ 40
- 60%, and < 40%

Partial adherence
Poor adherence36
Inadequate adherence
Non-compliance

Persistence

Early-stage Persistence6

Definition of time period allowed or
considered between the new
prescription dispensing and the one
refill.

Point-of-Time
Persistence
Early Persistence
Persistence37

Early-stage Non-
persistence10,38

Failure to have the new
prescription refilled over a
time period consistent with
current use of the drug

Definition of time period allowed or
considered between the new
prescription dispensing and the one
refill.

Early Non-persistence

Later-stage Persistence

Two or more refills (i.e., the
new prescription was
dispensed and at least 2 refills
of that prescription were
dispensed) over a time period
consistent with current use of
the drug. The time period can
span several refills that occur
over 6 months, 12 months, or
longer

Definition of time periods allowed
or considered between refills; can
include definition of time period
allowed after last refill in the
measurement period

Second stage persistence6
Refill compliance
Persistent/Persistence31,38-
43

Later-stage
Non-persistence

Definition of time periods allowed
or considered between refills; can
include definition of time period
allowed after last refill in the
measurement period

Second stage non-
persistence
Suboptimal persistence
Not persistent
Non-persistence34,44

Discontinuation

Discontinuation32,45-48

Failure to have a medication
dispensing within a defined
number of days after
exhaustion of the days' supply
of the previous dispensing
(often includes exhaustion of
any stockpiled medication
accumulated from previous
dispensings)

Definition of "defined number of
days after exhaustion of the days'
supply of the previous dispensing;"
180 days often used

Termination40
End of therapy

TABLE 2

Type and Name of Measure

Description and Calculation

Strengths

Weaknesses

Based on Medication Possession*

Medication Possession Ratio
(MPR) and Medication
Possession Ratio Modified
(MPRm)18,23,26,28

Estimate of proportion (or
percentage) of days' supply
obtained during a specified time
period or over a period of refill
intervals. Ratio of total days'
supply to number of days in
observation period

Variations:

1) Most common: Days' supply of medication dispensed during a specified observation period (e.g., 1 year), divided by number of days in observation period from first dispensing to end of observation period; multiplied by 100 to obtain percent

2) Also known as MPR modified (MPRm): Days' supply of medication dispensed during specified observation period excluding last refill, divided by number of days between first and last dispensing; multiplied by 100 to obtain percent

3) Less common: Days' supply of medication dispensed during specified observation period from first to last dispensing, divided by number of days between first and last dispensing PLUS days' supply dispensed with last dispensing; multiplied by 100 to obtain percent (This has also been referred to as MPRm)

4) Uncommon: Days' supply dispensed during observation period; divided by number of days until next dispensing

Mathematically similar to MRA
and CMA

Categorical or continuous

Can be averaged across study patients to determine overall study adherence value

Calculation variations that define the interval from the first dispensing until the end of a measurement period do account for a patient's discontinuation of medication (example: variation #1)

Provides nearly identical results to PDC when examining adherence to a single drug

Confusion in literature due to multiple calculation methods

Cannot be calculated for patients with primary non-adherence (i.e., those with no first dispensing do not appear in claims databases); systematically excludes the most non-adherent patients

Calculation for those with only one dispensing is imprecise (requires a complete, bounded dispensing interval and the initial days' supply to calculate)

Improved precision when calculated over at least 3 dispensings

If number of days of observation period differ across patients, ratios cannot be combined (must be divided and averaged to provide overall study adherence value)

Calculation variations that define the interval as the time between the first dispensing and the last dispensing do not account for medication discontinuation (example: variation #2)

Likely to overestimate true rate of adherence when a patient receives early refills which may result in an "extra fill" during a measurement interval. If ratio is not capped at 1.0, then reports on average MPR will be skewed upwards

When calculated for a medication class, a switch between medications in the same class during the interval, with an overlap of the new drug with the prior drug, will inflate the MPR. A similar situation occurs when a patient takes more than one medication concurrently from within the same class

Medication Refill Adherence
(MRA)23,28

MEDSUM49

Number of daily doses dispensed
in a period divided by number of
days in period; if applied
continuously, measures
continuous med acquisition.

Proportion of
Days Covered (PDC)18,23,26-28

Total number of days' supply
dispensed during specified
observation period divided by
number of days in patient's
observation period (i.e., this
denominator is number of days
between first dispensing during
observation period and end of the
observation period) multiplied by
100 to obtain percent; capped at 1

Rather than summing days'
supply across multiple drugs,
should create time arrays to
reflect dates encompassed by
each dispensing of each drug
within the patient's observation
period

Categorical or continuous

Mean of each patient's PDC provides an overall study adherence value

SAS code available for calculating time arrays50

Provides similar results to MPR when examining single drug adherence

Provides a more conservative estimate of adherence when examining adherence to drugs with frequent switches and concomitant therapy with multiple drugs within a class

Adjustment for inpatient hospital stays does not significantly alter population adherence estimate

Cannot be calculated for patients with primary non-adherence; systematically excludes the most non-adherent patients

Calculation for those with only one dispensing is imprecise

Improved precision when calculated over at least 3 dispensings

Ignores non-adherent time after the last refill

Underestimates non-possession within refill intervals if followed by subsequent early dispensings

Continuous multiple-refill-
interval
measure of Medication
Availability OR Continuous
measure of Medication
Acquisition (CMA)23,28

Total days' supply of medication
obtained throughout study period
divided by number of days from
first dispensing until study
completion date (number of days
in observation period); mean of
each patient's CMA value
provides overall study adherence
value

Mathematically similar formulas
include MRA and MPR

Cannot be calculated for patients with primary non-adherence; systematically excludes the most non-adherent patients

Calculation for those with only one dispensing is imprecise

Improved precision when calculated over at least 3 dispensings

Ignores non-adherent time after last refill

Continuous, Single interval
measure of medication
Acquisition (CSA)28

Single-interval measure of
medication availability; provides
an adherence value for each
patient between dispensings (not
overall study period); mean of all
dispensing adherence values
provides overall study adherence
value

Number of days' supply
dispensed divided by number of
days in interval from dispensing
date up to (not including) next
dispensing date

Compliance Rate or Compliance
Ratio (CR)23

Sum of days' supplies for each
patient, minus days' supply
obtained at last dispensing
divided by number of days from
first up to (not including) last
dispensing date

Denominator can also be listed
as: "last claim date minus index
date"

Based on Medication Gaps*

New Prescription Medication
Gap (NPMG)6

Time between date provider first
prescribes medication until first
of the following: end of follow-
up, censoring due to patient being
switched to alternate therapy or
medication discontinued by
prescriber; total days without
sufficient supply summing across
each refill interval within follow-
up period, divided by total
number of days from point of a
new electronic prescription to end
of follow-up

Can assess primary (non) adherence and patients with only one dispensing

Evaluates medication supply starting at prescribing (rather than dispensing) and ending at a fixed point. More comprehensively captures non-adherence than other measures

Quantifies non-adherent time after discontinuation or after exhaustion of stockpiled medication

Categorical or continuous

Primarily useful for assessing adherence to newly-prescribed medication rather than adherence of existing medication use

Continuous measure of
Medication Gaps (CMG)28,51

Subtract total days' supply
obtained throughout study period
from total number of days of
observation period (gives number
of days of treatment gaps); total
days of treatment gaps is then
divided by number of days of
observation period

The mean of each patient's CMG
value provides an overall study
non-adherence value based on
lack of available medication; 0%
reflects complete adherence and
100% reflects complete non-
adherence

Sums proportion of days without medication across all refill intervals starting with first dispensing and ending with last dispensing, providing a cumulative assessment of gaps in supply

Cannot be calculated for patients with primary non-adherence; systematically excludes the most non-adherent patients

Calculation for those with only one dispensing is imprecise

Improved precision when calculated over at least 3 dispensings

Provides negative values where patient obtained days' supply exceeds days of study participation

Assumes discontinuation after last dispensing once stockpiled medications exhausted (Note: a modified CMG has been developed that accounts for stockpiling)48

Person-time censored at last fill date

Continuous Multiple interval
measure of OverSupply
(CMOS)23,28

Total number of days' supply (if
gap) or surplus divided by days in
observation period or total days to
next fill

Mean of each patient's CMOS
value provides an overall study
non-adherence value

Cannot be calculated for patients with primary non-adherence; systematically excludes the most non-adherent patients

Calculation for those with only one dispensing is imprecise

Can provide negative values

Ignores non-adherent time after last refill

Underestimates gaps within refill intervals if they are followed by subsequent early dispensing dates

Developing the Conceptual Model of Standardized Terminology and Definitions of Medication Adherence for Electronic Database Methods

The overall intent of developing a conceptual model was to set out a series of general definitions that could be broadly applied across the fields of electronic data methods and medical informatics and that were applicable to estimating adherence to oral medications for chronic diseases. These definitions were intended to encompass the many opportunities for patients to accept or decline medication within the prescribing and dispensing components of the adherence, persistence, and discontinuation continuum of behaviors. In developing the conceptual model, we applied the following principles: 1) Develop a clear, concise, and broadly applicable model that described a sequence of discrete behaviors over time, 2) Articulate key constructs and sub-constructs of the continuum, 3) Express and organize categories in a logical and systematic manner, 4) Provide sufficient detail to clarify the approach and enable its use, and 5) Avoid restricting appropriate study-specific decisions (e.g., observation windows).

Adherence and persistence definitions require study-specific decisions to yield operational definitions of, for example, the period of observation ("observation window"), the patient sample under study, and the period over which the prescription must be filled after the initial order is written. The duration of the observation window is often conditional on the context, type of medication or disease state, unique details (e.g., usual days' supply of drug dispensed) and selection criteria (e.g., patients with at least two dispensings). Other study-specific decisions often include specifying an observation timeframe after the medication was dispensed as well as measurement metrics and tools such as formulas to calculate medication possession or gaps in medication availability. The definitions outlined here are focused on the structure of the definitions rather than on the specifics of the operational definitions. However, some operational definitions are provided to remind researchers that these decisions must also be clearly documented. Although the primary focus of developing this conceptual model was to provide broadly applicable definitions, we also identify and classify existing metrics that enable use of these definitions. The Kaiser Permanente Colorado Institutional Review Board (IRB) determined that the activities involved in writing this paper met the federal and institutional criteria for exemption from IRB review.

RESULTS

Our proposed conceptual model, terminology and definitions of medication adherence for electronic-data-based methods ( Figure 1 and Table 1 ) is predicated on two key constructs: medication adherence and medication persistence. Adherence connotes the degree or extent to which the patient conforms to the medication use recommendations specified by the prescriber (e.g., frequency/interval of administration, time of day ingested, strength of dosage).20 In contrast, persistence encompasses the time over which a patient continues treatment or continues to re/fill the prescription, from starting to stopping therapy.20,22

In this conceptual model ( Figure 1 ), adherence is subdivided into two main categories: Primary adherence and secondary adherence. Primary adherence is a discrete event that assesses whether or not the patient received the first prescription. In contrast, secondary adherence is an ongoing process that measures whether or not the patient received dispensings or refills as prescribed during a defined observation period. Only after these adherence sub-constructs have been acknowledged and assessed can the next level of secondary adherence-whether it is adequate or inadequate-be assessed. In these proposed definitions, the converse of each adherence term is simply "non" as in "primary non-adherence" and "secondary non-adherence."

Medication persistence implies that the patient must have exhibited at least primary adherence because persistence over time cannot be measured unless the patient has received at least the first dispensing ( Figure 2 ). We propose that early-stage persistence be defined to include individuals with at least two dispensings and later-stage persistence as including individuals with three or more dispensings of the medication and with evidence of medication availability. For consistent terminology, we propose the converse of each persistence category to also be "non" as in "early-stage non-persistence" and "later-stage non-persistence."

Medication discontinuation implies that a patient has terminated therapy as evidenced by not refilling a prescription, but no subjective inference regarding appropriateness is made, since discontinuation may be initiated either by the clinician or the patient. Further, in claims database studies, it is usually not possible to determine whether discontinuation was prescriber-initiated or patient-initiated. Medication discontinuation in electronic database studies can only be assessed within the context of a pre-specified operational definition for the required number of days without medication available. Thus, very low measured levels of adherence (MPR or PDC < 40%; CMG or NPMG > 60%) can in some circumstances represent, or be confused with, discontinuation.

Metrics that have been used to calculate medication adherence and/or persistence using electronic databases are summarized in Table 2 . In general, these metrics enable calculation of either medication possession (i.e., possession measures) or gaps in medications availability (i.e., gap measures)18,23-25 and most estimate adherence only among individuals with secondary adherence. Most metrics are continuous measures, but they are often categorized (e.g., low or inadequate versus moderate versus high or adequate adherence). These measures require data including the date of medication dispensing, days' supply dispensed with each dispensing, and previous (stockpiled) medications (or an indication that it will be set to zero) to estimate medication availability and consumption, usually estimated between the first and terminal dispensings within an observation window. A minority of metrics estimate availability within a single dispensing interval (e.g., the Continuous, Single interval measure of medication Acquisition, CSA). The metrics also vary in whether or not the days' supply dispensed with the terminal dispensing is included in the calculation. The time between any one dispensing and the subsequent dispensing is known as the refill interval. Person-time is censored at the last dispensing date, at the time of exhaustion of the last days' supply, or at a fixed number of days after exhaustion of the last days' supply. Most gap measures of secondary adherence censor after the last dispensing once stockpiled medications have been exhausted.

The two most commonly used secondary adherence medication possession measures are the Medication Possession Ratio (MPR) and the Proportion of Days Covered (PDC),18,22,23,26-28 Both report medication availability by estimating the proportion of prescribed days' supply obtained during a specified observation period over refill intervals and both are becoming widely applied in health care settings29 in large part because they are easily calculated (a SAS macro has been written for MPR and PDC). For example, as operationalized by the Pharmacy Quality Alliance,29 the PDC has been endorsed by the National Quality Forum as a tool to measure health care quality.30 The main difference between the PDC and the MPR is that with the PDC any oversupply is truncated, whereas adherence values of greater than 100 percent are allowed with the MPR. There is controversy about whether "over adherence," often considered as MPR between 100 and 120 percent, has clinical meaning.26 A shortcoming of these (and other) secondary adherence measures is that, when integrating across several observation periods of multiple refills each, delayed dispensing(s) in one observation period can be numerically counterbalanced by early dispensing in a later observation period, thus potentially under-ascertaining adherence in one observation period and overestimating it in another. The converse can also occur. This drawback is of particular importance in longitudinal assessments where changes in adherence behavior are assessed across multiple observation periods by calculating the adherence metric separately within each period. Other strengths and weaknesses of the medication possession and gap measures are summarized in Table 2 .

Because most measures of adherence require at least two dispensings, the least adherent patients (primary non-adherent) are excluded. Within the last few years measures have been developed that include patients with either primary or secondary (non-)adherence.6 One such metric, the New Prescription Medication Gap (NPMG) measure, is defined as the proportion of days within an interval bounded by the prescriber's initial EHR prescription medication order date and the end of the observation period (or end of follow-up if censored or the therapy is switched or discontinued).6 As with older gap measures, NPMG is a continuous measure, ranging from one hundred percent for patients who obtain no medication to zero percent for those who consistently refill their medication in a timely fashion. Unlike secondary adherence measures, NPMG was designed to evaluate medication supply starting at prescribing and ending at a fixed censoring point, thus comprehensively capturing (non)-adherence for those who never start the prescribed medication or who discontinue it early as well as for those who have at least two dispensings. An additional strength of NPMG is that because it enables evaluation from the point of prescribing in the EHR, person-time can be censored if the prescriber switches or discontinues therapy and documents those orders in the EHR.

DISCUSSION

In this paper we offer a standardized set of definitions and terminology for assessing medication adherence and persistence in electronic database studies, whether the databases employed are medications ordered within an EHR, medications dispensed and documented in a pharmacy database, or pharmacy claims processed through an insurance database. These conceptual models and terminology are more comprehensive than current, commonly used definitions. The models we propose include clear and systematic definitions of adherence and persistence developed to facilitate EHR-based research, are specific to medication adherence, and extend to adherence and persistence subcategories and medication discontinuation. We also point out the importance and utility of developing precise operational definitions for adherence research as these definitions enhance the precision of ascertaining whether a patient was likely exposed to a specific medication on a particular date for a specific research purpose (e.g., on the date of some clinical measure, event, or outcome).

Research focusing on adherence is voluminous. As obtaining the initial prescription medication and taking the medication are prerequisite health behaviors for medication effectiveness, these are key explanatory variables when observed effectiveness is lower than the efficacy demonstrated in controlled trials. As a consequence, comparative effectiveness studies may be designed to evaluate the effectiveness of various interventions to improve these adherence behaviors in their own right. Meta-analytic studies are also useful in assessing adherence as an outcome, and the level of adherence necessary to achieve treatment goals. To facilitate these types of studies, it is critical that the adherence measure be accurately estimated and consistent across studies.

There are limitations to this work. Our conceptual model is specific to prescription-filling and has not been compared with frameworks for other prescription behaviors such as medication-taking.20,31 Definitions are only part of the decision-making process in adherence research. Many important methodological considerations that should be addressed were beyond the scope of this paper such as identifying clinically meaningful categorizations for adherence (e.g., < 20% CMG; >= 80% MPR) based on observed relationships between adherence and clinical outcomes for specific disease states, the role of informative censoring (e.g., medication stop orders and switches), comprehensive assessment of discrete events (e.g., appropriate time frames to consider for medication discontinuation), and bias associated with assuming dispensing data are complete (e.g., prescriptions transferred outside an integrated healthcare system, paying cash for prescriptions resulting in no prescription insurance claims being filed).

CONCLUSION

We offer a set of standardized medication adherence terminology and definitions for use with electronic database research. The medication adherence and persistence conceptual models and definitions we present will enable future meta-analytic and comparative effectiveness research, as standardized terminology facilitates rigorous comparisons. As such, this paper is foundational for adherence methods.

Footnotes

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

# data[].attributes.attachment_link.title
Standardizing Terminology and Definitions of Medication Adherence and Persistence in Research employing Electronic Databases

# data[].attributes.attachment_link.description
Med Care. Author manuscript; available in PMC 2014 Aug 1. Published in final edited form as: Marsha A. Raebel, PharmD, 1, 2 Julie Schmittdiel, PhD, 3 Andrew J. Karter, PhD, 3 Jennifer L. Konieczny, PharmD, 4 and John F.

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Theory 1 Topic 4.1.1 Data Information and Knowledge

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Take away:
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The TIGER International Task Force began comprehensive activities to compile recommended core international informatics competencies reflective of many countries, scientific societies, and research projects in three phases that resulted in two recommendation frameworks focused global health informatics interprofessionals.

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AACN- American Association of Colleges of Nursing

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