Evidence map›Paper›PMID 41726508›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

STop Clock for Automated Tracking (STAT) during Time-Critical Medical Work: Evaluating the Accuracy and Usability of an AI-Driven Automated Stop Clock.

Katherine A Zellner, Sifan Yuan, Emily R Ernst, Dylan W Arkowitz, Aaron H Mun, Mary S Kim, Ivan Marsic, Randall S Burd, Aleksandra Sarcevic

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Katherine A ZellnerDrexel University, Philadelphia, PA, USA.
Sifan YuanRutgers University, New Brunswick, NJ, USA.
Emily R ErnstDrexel University, Philadelphia, PA, USA.
Dylan W ArkowitzChildren's National Hospital, Washington D.C., USA.
Aaron H MunChildren's National Hospital, Washington D.C., USA.
Mary S KimChildren's National Hospital, Washington D.C., USA.
Ivan MarsicRutgers University, New Brunswick, NJ, USA.
Randall S BurdChildren's National Hospital, Washington D.C., USA.
Aleksandra SarcevicDrexel University, Philadelphia, PA, USA.

Funding

Intention-aware Recommender System for Improving Trauma Resuscitation OutcomesR01LM011834 · NLM · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., MARSIC, IVAN · 2014 to 2023
$4.7M
NLM NIH HHS R01 LM011834
6 · The paper itself

Abstract

Delays and process inefficiencies during trauma resuscitation can contribute to adverse patient outcomes. While tracking elapsed time may improve the trauma team's temporal awareness and reduce delays, reliance on manual activation of stop clocks can introduce variability. To address this limitation, we implemented a computer vision-powered automatic stop clock designed to activate upon patient arrival without requiring manual input. We conducted a retrospective video review of 50 trauma resuscitations to assess how the clock was used in practice, followed by semi-structured interviews with nine trauma team members to elicit their feedback and perceptions. This study contributes to the broader discussion on AI-assisted clinical tools, highlighting the role of automation in supporting trauma teams, reducing variability in time tracking, and improving process efficiency.

Indexed as

Artificial IntelligenceResuscitationHumansIntelligent SystemsRetrospective StudiesVideo Recording

Identifiers

PMID41726508
PMCPMC12919504

What OpenQuestion holds

Textmetadata
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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.