Modeling workflow, operational, and financial implications of AI-enabled same-day diagnostic follow-up for screening mammograms

Abstract

While much of the evaluation of artificial intelligence (AI) in healthcare has focused on technical performance metrics such as accuracy or area under the curve, real-world adoption critically depends on how AI reshapes clinical workflows, operations, and revenue streams. Simulation models provide a means to anticipate these impacts before implementation, allowing stakeholders to weigh benefits against potential harm. In this study, we used discrete-event simulation to evaluate an AI-assisted workflow for same-day diagnostic breast imaging following abnormal screening mammograms. The revised workflow captured an additional of 1.1% mammography screening patients who might otherwise be lost to follow-up. It also eliminated the need for a second visit for diagnostic workup for 11% of mammography screening patients. It also increased daily work relative value units by 4.8%, translating to an estimated $15,979 in additional annual gain, while extending clinic operating hours by 2.9%, amounting to 109.5 hours annually. These findings highlight how simulation modeling can inform the operational and financial implications of AI adoption in imaging workflows in clinical practice.

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