Roger Daglius Dias, MD, PhD, MBA

Intelligent Interruption Management System to Enhance Safety and Performance in Complex Surgical and Robotic Procedures

Citation:

Roger D Dias, Heather M Conboy, Jennifer M Gabany, Lori A Clarke, Leon J Osterweil, David Arney, Julian M Goldman, Giuseppe Riccardi, George S Avrunin, Steven J Yule, and Marco A Zenati. 2018. “Intelligent Interruption Management System to Enhance Safety and Performance in Complex Surgical and Robotic Procedures.” OR 2.0 Context Aware Oper Theaters Comput Assist Robot Endosc Clin Image Based Proced Skin Image Anal (2018), 11041, Pp. 62-68.

Abstract:

Procedural flow disruptions secondary to interruptions play a key role in error occurrence during complex medical procedures, mainly because they increase mental workload among team members, negatively impacting team performance and patient safety. Since certain types of interruptions are unavoidable, and consequently the need for multitasking is inherent to complex procedural care, this field can benefit from an intelligent system capable of identifying in which moment flow interference is appropriate without generating disruptions. In the present study we describe a novel approach for the identification of tasks imposing low cognitive load and tasks that demand high cognitive effort during real-life cardiac surgeries. We used heart rate variability analysis as an objective measure of cognitive load, capturing data in a real-time and unobtrusive manner from multiple team members (surgeon, anesthesiologist and perfusionist) simultaneously. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the identification of specific steps, substeps and tasks that impose low cognitive load. An interruption management system can use these low demand situations to guide the surgical team in terms of the appropriateness of flow interruptions. The described approach also enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies) or in situations that are prone to errors. An in-depth understanding of the relationship between cognitive overload states, task demands, and error occurrence will drive the development of cognitive supporting systems that recognize and mitigate errors efficiently and proactively during high complex procedures.

Last updated on 07/08/2021