Evidence map›Paper›PMID 38635318›Full record

ArticleJMIR human factors2024

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls.

Hanna J Barton, Apoorva Maru, Margaret A Leaf, Daniel J Hekman, Douglas A Wiegmann, Manish N Shah, Brian W Patterson

Abstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Barriers to and Facilitators of Technology Adoption in Emergency Departments: A Comprehensive Review.International journal of environmental research and public health · 2025
    Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hanna J BartonBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0003-0457-1153
Apoorva MaruBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0001-6184-6446
Margaret A LeafDepartment of Information Services, UW Health, Madison, WI, United States.ORCID 0000-0001-8076-7110
Daniel J HekmanBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-7964-9501
Douglas A WiegmannDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-5604-9853
Manish N ShahBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0001-6331-1074
Brian W PattersonBerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-4584-3808

Funding

Preventing Future Falls in Older Adult ED Patients: Evaluating the Implementation and Effectiveness of a Novel Automated Screening and Referral InterventionR18HS027735 · AHRQ · UNIVERSITY OF WISCONSIN-MADISON · PI PATTERSON, BRIAN W · 2021 to 2025
$1.7M
AHRQ HHS R18 HS027735
6 · The paper itself

Abstract

backgroundClinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation.

objectiveThis study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data.

methodsPreviously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool.

resultsThe following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to.

conclusionsOur study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

Indexed as

Decision Support Systems, ClinicalEmergency Service, HospitalAmbulatory Care FacilitiesData AccuracyHumansacademic detailingCDSCDS toolclinical careclinical decision supportdepartment-basedED environmenteducational outreachEHRelderelderlyelectronic health recordemergency departmentemergency medicineevidence-basedfall-risk predictiongeriatricgeriatricsgerontologyhealth IThigh-risk patienthigh-risk patientshuman factorsimplementation methodinterviewmachine learningolder adultolder adultsolder peopleolder personpharmaceuticalpharmaceutical salespreventative interventionSEIPSSystems Engineering Initiative for Patient Safetyteam-based analysisUnited Stateswork systems

Identifiers

PMID38635318
PMCPMC11066751

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.