Human-Centered Development of an Explainable AI Framework for Real-Time Surgical Risk Surveillance

Background: Artificial Intelligence (AI) clinical decision support (CDS) systems have the potential to augment surgical risk assessments, but successful adoption depends on an understanding of end-user needs and current workflows. This study reports the initial co-design of MySurgeryRisk, an AI CDS tool to predict the risk of nine post-operative complications in surgical patients. Methods: Semi-structured focus groups and interviews were held as co-design sessions with perioperative physicians at a tertiary academic hospital in the Southeastern United States. Participants were read a surgical vignette and asked questions to elicit an understanding of their current decision-making practices before being introduced to the MySurgeryRisk prototype web interface. They were asked to provide feedback on the user interface and system features. Session transcripts were qualitatively coded, after which thematic analysis took place. Results: Data saturation was reached after 20 surgeons and anesthesiologists from varying career stages participated across 11 co-design sessions. Thematic analysis resulted in five themes: (1) decision-making cognitive processes, (2) current approach to decision-making, (3) future approach to decision-making with MySurgeryRisk, (4) feedback on current MySurgeryRisk prototype, and (5) trustworthy considerations. Conclusion: Clinical providers perceived MySurgeryRisk as a promising CDS tool that factors in a large volume of data and is computed in real-time without any need for manual input. Participants provided feedback on the design of the interface and imaged applications of the tool in the clinical workflow. However, its successful implementation will depend on its actionability and explainability of model outputs, integration into current electronic systems, and calibration of trust among end-users.