Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: a simulated, retrospective study

Abstract

Abstract Background Staffing shortages and inadequate healthcare access have driven the development of artificial intelligence (AI)-enabled tools in medicine. Accuracy of these algorithms has been extensively investigated, but research on downstream effects of AI integration into the clinical workflow is lacking. Objective We aim to analyze how integration of a basal cell carcinoma detection and tumor mapping algorithm in a Mohs micrographic surgery (MMS) unit may impact waiting times in the surgical pathology laboratory and on the floor. Methods Time spent on each task and slide, staff, and histotechnician waiting times were analyzed over a 20 day period in a MMS unit. A simulated AI workflow was created and the time differences between the real and simulated workflows were compared. Results Simulated addition of the algorithm led to improvements of 64% in slide waiting time (1:03:39 per case), 36% in staff waiting time (59:09 per case), and 25% in histotechnician waiting time (25:27 per case). Limitations A single MMS unit was analyzed and AI integration was performed retrospectively, rather than in real time. Conclusions AI integration results in significantly reduced slide, staff, and histotechnician waiting time, which enables increased productivity and a streamlined clinical workflow. Capsule summary The accuracy of artificial intelligence algorithms has been well established. This study addresses the impact of implementation of such an algorithm into a real-world clinical workflow. Results indicate the potential for increased efficiency and productivity with use of artificial intelligence in Mohs micrographic surgery.

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