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

NCT05657002 | Completed

• 2 other identifiers: Amsterdam UMC 2019-AMC-JK-72019-AMC-JK-7
• Study Type: interventional
• Target: 160
• Locations: 1 country
• Sites: 1

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.

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

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• the total time to answer the two questions correctly, where the answer of medication B also includes the right motivation

  during the experiment/questionnaire

• the total time to answer the two questions correctly including the right motivations for medication A and B

  during the experiment/questionnaire

• the correctness of both the answer for medication A and B including the right motivations for A and B

  during the experiment/questionnaire

• the correctness of the answer for medication A including the right explanation

  during the experiment/questionnaire

Study Arms (2)

accurate problem list, then inaccurate problem list

ACTIVE COMPARATOR

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; Other: patient B with inaccurate problem list

inaccurate problem list, then accurate problem list

ACTIVE COMPARATOR

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; Other: patient B with accurate problem list

Interventions

patient A with accurate problem list OTHER

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.

accurate problem list, then inaccurate problem list

patient B with inaccurate problem list OTHER

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.

accurate problem list, then inaccurate problem list

patient A with inaccurate problem list OTHER

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.

inaccurate problem list, then accurate problem list

patient B with accurate problem list OTHER

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.

inaccurate problem list, then accurate problem list

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• Non-Dutch speaking employees as the patient cases and the exercises are described in Dutch

Sponsors & Collaborators

• Eva Klappe: lead

Study Sites (1)

Amsterdam UMC, Location AMC

Amsterdam, North Holland, 1105AZ, Netherlands

Location

Study Officials

• Eva Klappe, MSc

  Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Participant will be blinded, they do not know in what group they belong (thus which record they receive with the accurate or inaccurate problem list) and the participants are not informed that impact of problem list accuracy is investigated. An independent researcher produced a randomization schema where per block of 10 participants a balanced random order of "patient A with accurate problem list + patient B with inaccurate problem list" or "patient A with inaccurate problem list + patient B with accurate problem list" is produced. The investigator (ESK) follows this list for the consecutive participants and gives access to the appropriate patient records as defined by the randomisation schema. The independent researcher checks afterwards whether the right order is followed based on time stamps and randomisation schema. We performed a power analysis before conducting any experiment, which resulted in a required number of 157 participants.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: PhD Candidate

Model Details: 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.

Study Record Dates

• First Submitted: November 22, 2022
• First Posted: December 20, 2022
• Study Start: December 1, 2022
• Primary Completion: December 21, 2022
• Study Completion: December 21, 2022
• Last Updated: December 23, 2022

Record last verified: 2022-12

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)