Evidence map›Paper›PMID 40574936›Full record

ArticleAEM education and training2025

Computer-Based Competency Assessment in Point-Of-Care Ultrasound: A Systematic Review.

Lao-Tzu Allan-Blitz, Madeline Schwid, Nicole M Duggan, Rayan Ebnali Harari, Lauren Selame, Carrie Walsh, Katerina Papa, David Chu, Roger Dias, Andrew J Goldsmith

Abstract read
In one paragraph

Article 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. Review
  2. 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

10 authors.

Lao-Tzu Allan-BlitzDivision of Global Health Equity, Department of Medicine Brigham and Women's Hospital Boston Massachusetts USA.ORCID https://orcid.org/0000-0002-4077-3476
Madeline SchwidDepartment of Emergency Medicine University of Rochester Medical Center Rochester New York USA.
Nicole M DugganDivision of Emergency Ultrasound, Department of Emergency Medicine Brigham and Women's Hospital Boston Massachusetts USA.ORCID https://orcid.org/0000-0003-4829-4979
Rayan Ebnali HarariMedical AI & Cognitive Engineering (MAICE) Lab, STRATUS Center for Medical Simulation Brigham and Women's Hospital Boston Massachusetts USA.ORCID https://orcid.org/0009-0007-7494-0758
Lauren SelameDivision of Emergency Ultrasound, Department of Emergency Medicine Brigham and Women's Hospital Boston Massachusetts USA.
Carrie WalshDivision of Emergency Ultrasound, Department of Emergency Medicine Brigham and Women's Hospital Boston Massachusetts USA.ORCID https://orcid.org/0009-0005-3619-7129
Katerina PapaDepartment of Emergency Medicine, Lahey Hospital University of Massachusetts Medical School Burlington Massachusetts USA.
David ChuDivision of Emergency Ultrasound, Department of Emergency Medicine Brigham and Women's Hospital Boston Massachusetts USA.
Roger DiasMedical AI & Cognitive Engineering (MAICE) Lab, STRATUS Center for Medical Simulation Brigham and Women's Hospital Boston Massachusetts USA.
Andrew J GoldsmithDepartment of Emergency Medicine, Lahey Hospital University of Massachusetts Medical School Burlington Massachusetts USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Point of care ultrasound (POCUS) is a critical skill for physicians across multiple medical specialties, yet substantial heterogeneity exists in how competency is assessed. Computer-based approaches can be used to deliver, grade, and analyze learner performance, and may be more objective and reliable than traditional approaches using expert assessments. This study aimed to systematically review and summarize the existing literature surrounding computer-based approaches to assessing POCUS competency. Methods: We searched six online databases (MEDLINE, IEEE Xplore Digital Library, Association for Computing Machinery Digital Library, PsycINFO (Ovid), EMBASE, Web of Science Core Collection). We included original peer-reviewed studies that assessed computer-based metrics of POCUS competence among any learner group performing POCUS. We also reviewed reference lists of all included studies. We extracted data elements that included the specialty of participants, POCUS experience, POCUS modality used, and type and results of computer-based competency assessments. At least two authors conducted title and abstract screening, full text review, and data extraction, with discrepancies adjudicated by a third author. We present a qualitative synthesis of study findings. Results: Of 7375 identified studies, we included 28 in our final analysis. Computer-based metrics were used to assess knowledge ( Conclusion: We identified four computer-based metrics for assessing POCUS competence, many of which demonstrated consistent performance in distinguishing skill level. Further work is needed to standardize and validate those approaches.

Identifiers

PMID40574936
PMCPMC12187581

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Registered trials

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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.