Assessing Team Situational Awareness in the Operating Room via Computer Vision

TLDR

Situational awareness at individual and team levels is critical in the operating room, and the surgical safety checklist used during pre‑incision time‑outs has been shown worldwide to reduce intra‑operative complications and mortality. This study examined whether computer‑vision analysis of surgical videos could extract team motion metrics that differentiate teams with good versus poor situational awareness during the pre‑incision time‑out. We combined a validated observation‑based tool for assessing situational awareness with computer‑vision software that measured body position and motion patterns captured by off‑the‑shelf operating‑room cameras. The results demonstrated that entropy of the extracted motion metrics distinguishes teams with good and poor situational awareness, confirming that computer‑vision–based metrics can augment traditional performance assessments in the operating room.

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.