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• W4286572118 abstract "Situational awareness (SA) at both individual and team levels, plays a critical role in the operating room (OR). During the pre-incision time-out, the entire OR team comes together to deploy the surgical safety checklist (SSC). Worldwide, the implementation of the SSC has been shown to reduce intraoperative complications and mortality among surgical patients. In this study, we investigated the feasibility of applying computer vision analysis on surgical videos to extract team motion metrics that could differentiate teams with good SA from those with poor SA during the pre-incision time-out. We used a validated observation-based tool to assess SA, and a computer vision software to measure body position and motion patterns in the OR. Our findings showed that it is feasible to extract surgical team motion metrics captured via off-the-shelf OR cameras. Entropy as a measure of the level of team organization was able to distinguish surgical teams with good and poor SA. These findings corroborate existing studies showing that computer vision-based motion metrics have the potential to integrate traditional observation-based performance assessments in the OR." @default.
• W4286572118 created "2022-07-22" @default.
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• W4286572118 date "2022-06-06" @default.
• W4286572118 modified "2023-10-16" @default.
• W4286572118 title "Assessing Team Situational Awareness in the Operating Room via Computer Vision" @default.
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• W4286572118 doi "https://doi.org/10.1109/cogsima54611.2022.9830664" @default.
• W4286572118 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35994041" @default.
• W4286572118 hasPublicationYear "2022" @default.
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