Evidence map›Paper›PMID 40380469›Full record

ArticleStudies in health technology and informatics2025

Beyond Model Performance: Information Needs for an Algorithmovigilance Sociotechnical System.

Megan E Salwei, Sharon Davis, Laurie L Novak, Colin Walsh, Carrie Reale, Scott Nelson, Susannah Rose, Russ Beebe, Peter Embi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Studies in health technology and informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Megan E SalweiDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Sharon DavisDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Laurie L NovakDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Colin WalshDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Carrie RealeDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Scott NelsonDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Susannah RoseDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Russ BeebeDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Peter EmbiDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Funding

AHRQ HHS K01 HS029042
6 · The paper itself

Abstract

Proactive and ongoing monitoring of AI systems, or algorithmovigilance, is essential for mitigating patient safety risks from AI in healthcare. In this study, we describe the information needs for an AI monitoring and operations system and provide design guidelines for algorithmovigilance systems.

Indexed as

AlgorithmsArtificial IntelligencePatient SafetyHumansAlgorithmovigilanceArtificial intelligenceHuman-Centered Design

Identifiers

PMID40380469
PMCPMC13294864

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.