A Study to the Impact of Accuracy Problem Lists in Electronic Health Records on Correctness and Speed of Clinical Decision-making Performed by Dutch Healthcare Providers

Study Overview
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Brief Summary
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The primary objective of this study is to determine whether patient records with complete, structured and up-to-date problem lists ('accurate problem lists'), result in better clinical decision-making, compared to patient records that convey the same information in a less structured way where the problem list has missing and/or duplicate diagnoses ('inaccurate problem lists'). The secondary objective is to determine whether the time required to make a correct decision is less for patient records with accurate problem lists compared to patient records with inaccurate problem lists.

Detailed Description
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A problem list in Electronic Health Records (EHRs) is considered an essential feature in the collection of structured data. The problem list provides a centralized summary of each patient's medical problems and these problems or diagnoses are selected from the terminology underlying the problem list, such as SNOMED CT. If well maintained and structured, the problem list is a valuable tool for reviewing records of (unfamiliar) patients as it quickly shows the required information when needed. While studies have shown that the use of structured formats can serve as prompt for extra details, greater consistency of information and clinical decision-making there is little evidence whether a patient record with complete and structured problem lists results in more accurate and faster clinical decision making. In the study (ADAM's APPLE: Adequate Data registration And Monitoring, subproject: Accurate Presentation of Problem List Elements), the investigators will perform a crossover randomized controlled trial in which a laboratory experiment will be performed among individual healthcare professionals to assess the impact of patient records with accurate and inaccurate problem lists on clinical decision-making. The participants will be presented with two records of two different patients in a training environment of the software system EPIC, one of them with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicate information. The participants do not know which of the two records includes the accurate problem list and which record includes the inaccurate problem list. Participants are asked to decide whether or not to prescribe two medications for those two patients. One medication is not allowed per patient because the patient is allergic to that medication, which is documented on the allergy list. For the first patient record, the other medication is not allowed, because of a contraindicated diagnosis and for the second patient record the other medication is not allowed, because of a side effect that has occurred using that medication in the medical history. Based on the correctness of the motivation for correct answers and the time to the right answers, the research question if accurate problem lists in patient records lead to better and faster decision-making is answered. Prior to this study, two healthcare professionals in the research team determined suitable use cases and questions for this study. These use cases were based on real-world unstructured versions of patient records. Two optimized accurate problem lists were also created for both patient records, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list).

Official Title
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a Randomized Controlled Trial Study to Determine the Impact of Accuracy of Problem Lists in Electronic Health Records on Clinical Decision-making

Conditions
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Clinical Decision-Making, Decision Making, Computer-assisted, Medical Records, Problem-Oriented, Quality of Health Care, Evidence-Based Practice, Data Accuracy, Documentation / Standards, Documentation / Statistics & Numerical Data, Forms and Records Control / Standards, Humans, International Classification of Diseases / Standards

Intervention / Treatment
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* Other: patient A with accurate problem list
* Other: patient B with inaccurate problem list
* Other: patient A with inaccurate problem list
* Other: patient B with accurate problem list


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Healthcare professionals who are allowed to prescribe medication, thus hold a position as: medical specialist, medical resident, nurse specialist or physician assistant, research-specialists Healthcare professionals must have followed at least the 'basic EHR Epic course'. This electronic health record course lasts for three days and includes how to send letters, register diagnoses in a record, request testing, all in the software system EPIC, which concludes with an exam on the theory. Exclusion Criteria: Non-Dutch speaking employees as the patient cases and the exercises are described in Dutch
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Crossover Assignment
Interventional Model Description: Participants will be presented with 2 patient records in EPIC, one with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicates. Each participant answers a total of 4 questions for 2 separate patient cases (2 questions per case), one of those cases having the inaccurate and the other having the accurate problem list. Using 2 separate patient cases rather than one was decided to prevent a memory effect, i.e., participants would have had time to understand the patient's conditions described in the record which could impact the time required to answer questions in round 2. Additionally, the order in which patients cases are provided is not randomized, since in practice it also happens that a new patient is followed up by another new patient which requires professionals to systematically go through two separate records with little time in between.
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: accurate problem list, then inaccurate problem list<br>in round 1, the participants will use the patient record of patient A, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (on problem list) In round 2, the participants will use the patient record of patient B, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (not on problem list) | Other: patient A with accurate problem list<br>* A problem list that contains a diagnosis code that is contraindicated with a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis relevant for the question asked.<br>Other: patient B with inaccurate problem list<br>* A problem list that does not contain the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes. The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses.<br>|
| Active Comparator: inaccurate problem list, then accurate problem list<br>in round 1, the participants will use the patient record of patient A, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (not on problem list) In round 2, the participants will use the patient record of patient B, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (on problem list) | Other: patient A with inaccurate problem list<br>* A problem list that does not contain the diagnosis code that is contraindicated with the type of medication (Y). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes. The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses.<br>Other: patient B with accurate problem list<br>* A problem list that contains the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis and details relevant for the question asked.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| the correctness of the answer of medication B including the right motivation | Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: can the patient be prescribed medication A and/or B?. Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication B. | during the experiment/questionnaire | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| the total time to answer the two questions correctly, where the answer of medication B also includes the right motivation | The time to enter the motivation was not measured. However, the participants might give the right answer but the wrong motivation. For this secondary outcome measure, only the answers that have the right motivation are considered as 'right answer'. | during the experiment/questionnaire | 
| the total time to answer the two questions correctly including the right motivations for medication A and B | A time stamp is registered when the participant opens the question, and a time stamp is registered when the participant confirms the answers. A time stamp is therefore registered for the total of two Yes/No answers per patient record. | during the experiment/questionnaire | 
| the correctness of both the answer for medication A and B including the right motivations for A and B | Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: can the patient be prescribed medication A and/or B?. Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication A and B. | during the experiment/questionnaire | 
| the correctness of the answer for medication A including the right explanation | Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: can the patient be prescribed medication A and/or B?. Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication A. | during the experiment/questionnaire |