A sensor-based framework for layout and workflow assessment in operating rooms

Due to their large sizes and impediments to personnel workflows, integrating robotic technologies into the existing operating rooms (OR) is a challenge. In this study, we developed an ultra-wideband sensor-based human-machine-environment framework for layout and workflow assessments within the OR. In addition to providing best practices for use of the framework, we also demonstrated its effectiveness in understanding layout and workflow inefficiencies in 12 robotic-assisted surgeries (RAS) across 4 different surgical specialties. We found avoidable movements as the circulating nurse covers at least twice the distance of any other OR personnel before the patient cart (robot) is docked. OR areas of congestion and undesirable personnel-pair proximities across RAS phases that impose extra non-technical skill challenges were determined. Our findings highlight several implications for the added complexity of integrating robotic technologies into the OR, which can serve as drivers for objective evidence-based recommendations to combat RAS OR layout and workflow inefficiencies. •A sensing framework was developed for layout and workflow assessment in surgeries.•Best practices for capturing and interpreting surgical sensor data was provided.•Framework was demonstrated using 12 robotic-assisted surgeries.•Circulating nurse covers more distance than any other personnel in pre-docking.•Personnel pair undesirable proximities impose non-technical skill challenges.