Please join us in welcoming Shannon Harris an associate professor from the Virginia Commonwealth University’s School of Business.
https://ncsu.zoom.us/j/92654718507?pwd=emJZV2xKa3RNekROVFFxWmVHclpOUT09
Meeting ID: 926 5471 8507
Passcode: 643620
Overbooked And Overlooked: Machine Learning And Racial Bias In Medical Appointment Scheduling
Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them in a way that is least disruptive to the clinic. Those scheduling decisions maximize the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled more frequently into less desirable slots, and spend more time waiting in the clinic for their appointments. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than non-black patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective.
Shannon L. Harris is an Assistant Professor of Supply Chain Management and Analytics at the School of Business at Virginia Commonwealth University. She earned a PhD in Business Analytics and Operations from the University of Pittsburgh in 2016. Her research interests include mathematical and empirical modeling with a focus on healthcare applications. Primarily, she analyzes the attendance behavior of patients to outpatient clinic appointments, and how that behavior affects a clinic’s scheduling practices. Additionally, she has a research stream regarding racial bias in healthcare scheduling. Her work was been published in the European Journal of Operational Research, Manufacturing and Service Operations Management, Military Medicine, and Decision Support Systems among others.
