ReviewAEM education and training2025
Resident clinical dashboards to support precision education in emergency medicine.
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.
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.
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.
Who cites it
2 citing papers in PubMed.
- Education Research: Educational Informatics: Characterizing Child Neurology Resident Clinical Experiences Using Electronic Health Record Data.Neurology. Education · 2026Article
- Resident clinical dashboards to support precision education in emergency medicine.AEM education and training · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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
What OpenQuestion holds
Registered trials
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.