Evidence map›Paper›PMID 40308868›Full record

ReviewAEM education and training2025

Resident clinical dashboards to support precision education in emergency medicine.

Joe-Ann S Moser, Nicholas Genes, Daniel J Hekman, Sara M Krzyzaniak, Timothy A Layng, Danielle Miller, Ashley C Rider, Selin T Sagalowsky, Moira E Smith, Benjamin H Schnapp

Abstract readReview
In one paragraph

Review in AEM education and training, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

10 authors.

Joe-Ann S MoserBerbeeWalsh Department of Emergency Medicine University of Wisconsin Madison Wisconsin USA.ORCID https://orcid.org/0000-0001-7018-2135
Nicholas GenesRonald O. Perelman Department of Emergency Medicine and Department of Pediatrics New York University Grossman School of Medicine New York New York USA.ORCID https://orcid.org/0000-0002-9836-2477
Daniel J HekmanBerbeeWalsh Department of Emergency Medicine University of Wisconsin Madison Wisconsin USA.ORCID https://orcid.org/0000-0002-7964-9501
Sara M KrzyzaniakDepartment of Emergency Medicine Stanford University California USA.ORCID https://orcid.org/0000-0002-8173-2750
Timothy A LayngDepartment of Emergency Medicine University of Virginia Charlottesville Virginia USA.
Danielle MillerDepartment of Emergency Medicine University of Colorado School of Medicine Aurora Colorado USA.ORCID https://orcid.org/0000-0003-3933-2355
Ashley C RiderDepartment of Emergency Medicine Stanford University California USA.
Selin T SagalowskyRonald O. Perelman Department of Emergency Medicine and Department of Pediatrics New York University Grossman School of Medicine New York New York USA.ORCID https://orcid.org/0000-0002-6923-146X
Moira E SmithDepartment of Emergency Medicine University of Virginia Charlottesville Virginia USA.
Benjamin H SchnappBerbeeWalsh Department of Emergency Medicine University of Wisconsin Madison Wisconsin USA.ORCID https://orcid.org/0000-0001-5031-8269

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the move toward competency-based medical education (CBME), data from the electronic health record (EHR) for informed self-improvement may be valuable as a part of programmatic assessment. Personalized dashboards are one way to view these clinical data. The purpose of this concept paper is to summarize the current state of clinical dashboards as they can be utilized by emergency medicine (EM) residency programs. Methods: The author group consisted of EM physicians from multiple institutions with medical education and informatics backgrounds and was identified by querying faculty presenting on resident clinical dashboards at the 2024 Society for Academic Emergency Medicine conference. Additional authors were identified by members of the initial group. Best practice literature was referenced; if none was available, group consensus was used. Categories of Metrics: Clinical exposures as well as efficiency, quality, documentation, and diversity metrics may be included in a resident dashboard. Resident dashboard metrics should focus on resident-sensitive measures rather than those primarily affected by attendings or systems-based factors. Considerations for Implementation: Implementation of these dashboards requires the technical expertise to turn EHR data into actionable data, a process called EHR phenotyping. The dashboard can be housed directly in the EHR or on a separate platform. Dashboard developers should consider how their implementation plan will affect how often dashboard data will be refreshed and how to best display the data for ease of understanding. Implications for Education & Training: Dashboards can provide objective data to residents, residency leadership and clinical competency committees as they identify areas of strength, growth areas, and set specific and actionable goals. The success of resident dashboards is reliant on resident buy-in and creating a culture of psychological safety through thoughtful implementation, coaching, and regular feedback. . Conclusion: Personalized clinical dashboards can play a crucial role in programmatic assessment within CBME, helping EM residents focus their efforts as they advance and refine their skills during training.

Identifiers

PMID40308868
PMCPMC12038736

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.