Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency

Oregon Health & Science University, Portland OR

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Abstract

PROJECT SUMMARY Physicians often report feeling pressured to see more patients to maintain revenue, while having less available time for patient care. Systematic data-driven methods for efficiently scheduling patients are important as physicians are pressured to see more and more patients. We propose that real time prediction models of patient visit lengths, the likelihood of missing appointments, and of patient wait times will help schedule patients more efficiently. Clinics will be able to safely overbook to avoid empty slots from missed appointments, have guidance for scheduling urgent add-on patients, and provide wait time estimates for patients when there are delays. We will develop methodologies for accessing data needed for these predictions in real time and propose that the integration of these models into workflows will improve scheduling accuracy, patient wait time, and patient satisfaction, while also increasing clinic volumes.