George S Avrunin, Lori A Clarke, Heather M Conboy, Leon J Osterweil, Roger D Dias, Steven J Yule, Julian M Goldman, and Marco A Zenati. 2018. “Toward Improving Surgical Outcomes by Incorporating Cognitive Load Measurement into Process-Driven Guidance.” Softw Eng Healthc Syst SEHS IEEE ACM Int Workshop, 2018, Pp. 2-9.
This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, “Smart” Checklist to help process performers understand and manage their pending tasks . This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations. This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.
Last updated on 07/08/2021