Project Details

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Improving Patient Scheduling for Respiratory Care - Team 2

Company: Cleveland Clinic Respiratory Institute

Major(s):
Primary: IE
Secondary: CMPSC

Non-Disclosure Agreement: NO

Intellectual Property: NO

Cleveland Clinic's Respiratory Institute provides world-class patient care by combining strengths in clinical care, research, and education. With over 170 pulmonologists, allergists/immunologists, infectious disease experts, and critical care specialists, the Respiratory Institute staff diagnose and treat over 200,000 patients annually with a wide spectrum of disorders in its outpatient offices, inpatient hospital floors, and intensive care units. Cleveland Clinic is ranked as one of the nation's top hospitals by U.S. News & World Report (2022-2023). The Cleveland Clinic Respiratory Institute seeks to improve its patient scheduling for better resource utilization and patient satisfaction. In addition, they would like to use machine learning/AI methods to better personalize the scheduling process and match patients and clinicians. Finally, they desire a simulation platform with which to estimate the impact of scheduling policies on a variety of patient outcomes. The Learning Factory team would utilize available data (much of it synthetically derived to ensure the protection of sensitive patient information) to develop and deliver an efficient, reliable, and generalizable strategy to effectively schedule patients and estimate the resulting impact compared to current practice. Successful development with the Respiratory Institute would establish a framework to improve the scheduling process across the entire Cleveland Clinic made up of over 15,000 medical professionals. The group will work with infectious disease specialist Shravan Kethireddy, MD, and IME faculty member Paul Griffin. It is desired that the group has some expertise in discrete event simulation (i.e., Simio), process modeling, and data science.