Clinical trial NCT05657002: 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
Official title: a Randomized Controlled Trial Study to Determine the Impact of Accuracy of Problem Lists in Electronic Health Records on Clinical Decision-making
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: 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
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
First posted: 2022-12-20
Enrollment: 160
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Intervention 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.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: 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: 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).

Eligibility criteria: 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

Primary outcomes:
measure: the correctness of the answer of medication B including the right motivation
timeFrame: during the experiment/questionnaire
Secondary outcomes:
measure: the total time to answer the two questions correctly, where the answer of medication B also includes the right motivation
timeFrame: during the experiment/questionnaire
measure: the total time to answer the two questions correctly including the right motivations for medication A and B
timeFrame: during the experiment/questionnaire
measure: the correctness of both the answer for medication A and B including the right motivations for A and B
timeFrame: during the experiment/questionnaire
measure: the correctness of the answer for medication A including the right explanation
timeFrame: during the experiment/questionnaire
Other outcomes:
Locations:
city: Amsterdam
country: Netherlands
facility: Amsterdam UMC, Location AMC
state: Noord-Holland
Study officials:
affiliation: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
name: Eva Klappe, MSc
role: PRINCIPAL_INVESTIGATOR
References: