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

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 and Aleksandra Sarcevic
AMIA ... Annual Symposium proceedings, v 2024, p1502
2024
PMID: 41726508

Abstract

Artificial Intelligence Humans Resuscitation Retrospective Studies Time Factors Video Recording Wounds and Injuries - therapy
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.

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