A Cognitive Modeling Approach to Decision Support Tool Design for Anesthesia Provider Crisis Management

Abstract

Prior research has revealed existing operating room (OR) patient monitors to provide limited support for prompt and accurate decision making by anesthesia providers during crises. Decision support tools (DSTs) developed for this purpose typically alert the anesthesia provider to existence of a problem but do not recommend a treatment plan. There is a need for a human-centered approach to the design and development of a crisis management DST. A hierarchical task analysis was conducted to identify anesthesia provider procedures in detecting, diagnosing, and treating a critical incident and a cognitive task analysis to elicit goals, decisions, and information requirements. This information was coded in a computational cognitive model using GOMS (Goals, Operators, Methods, Selection rules) Language. An OR monitor interface was prototyped to present output from the cognitive model following ecological interface design principles. A preliminary assessment of the DST was performed with anesthesiology and usability experts. The anesthesiologists indicated they would use the tool in the perioperative environment and would recommend its use by junior anesthesia providers. Future research will focus on formal validation of the DST design approach and comparison of tool output to actual anesthesia provider decisions in real or simulated crises.

ACKNOWLEDGMENTS

During the completion of this research, Noa Segall worked as a research assistant in the Edward P. Fitts Department of Industrial & Systems Engineering at North Carolina State University. This research was supported in part by an Information Technology Research grant from the National Science Foundation (NSF) (No. IIS-0426852). Melanie Wright's participation in this project was supported in part by a grant from National Institutes of Health (NIH), Agency for Healthcare Research and Quality (K02 HS015704-01). The opinions expressed are those of the authors and do not necessarily reflect the views of the NSF or NIH. We are grateful to the physicians who volunteered to participate in the interviews and to evaluate the decision support tool. We also thank Meghan Rogers for her conscientious work in formatting the article for submission for publication.