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 (ADAM's APPLE)

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

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.

Study Overview

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

Detailed Description

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).

• Study Type: Interventional
• Enrollment (Actual): 160
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Netherlands
  • Noord-Holland
    • Amsterdam, Noord-Holland, Netherlands, 1105AZ
      • Amsterdam UMC, location AMC

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: accurate problem list, then inaccurate problem list; 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; 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.; Other: patient B with inaccurate problem list; 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.
Active Comparator: inaccurate problem list, then accurate problem list; 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; 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.; Other: patient B with accurate problem list; 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.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Eva Klappe
• Investigators: Principal Investigator: Eva Klappe, MSc, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2022
• Primary Completion (Actual): December 21, 2022
• Study Completion (Actual): December 21, 2022

Study Registration Dates

• First Submitted: November 22, 2022
• First Submitted That Met QC Criteria: December 10, 2022
• First Posted (Actual): December 20, 2022

Study Record Updates

• Last Update Posted (Estimate): December 23, 2022
• Last Update Submitted That Met QC Criteria: December 21, 2022
• Last Verified: December 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: Amsterdam UMC 2019-AMC-JK-7; 2019-AMC-JK-7 (Other Grant/Funding Number: Amsterdam UMC)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No