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Assessing Team Situational Awareness in the Operating Room via Computer Vision.
Dias, Roger D; Kennedy-Metz, Lauren R; Yule, Steven J; Gombolay, Matthew; Zenati, Marco A.
Afiliación
  • Dias RD; Department of Emergency Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
  • Kennedy-Metz LR; Department of Surgery, Harvard Medical School, VA Boston Healthcare System, West Roxbury, MA, USA.
  • Yule SJ; Department of Clinical Surgery, University of Edinburgh, Edinburgh, Scotland.
  • Gombolay M; College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
  • Zenati MA; Department of Surgery, Harvard Medical School, VA Boston Healthcare System, West Roxbury, MA, USA.
Article en En | MEDLINE | ID: mdl-35994041
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: IEEE Conf Cogn Comput Asp Situat Manag Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: IEEE Conf Cogn Comput Asp Situat Manag Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos