Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Samorani, M., Harris, S.L., Blount, L.G., Lu, H. and Santoro, M.A., 2021. Overbooked and overlooked: machine learning and racial bias in medical appointment scheduling. Manufacturing & Service Operations Management.

19 Pages Posted: 23 Oct 2019 Last revised: 22 Jan 2022

See all articles by Michele Samorani

Michele Samorani

Santa Clara University - Information Systems and Analytics

Shannon Harris

Virginia Commonwealth University (VCU)

Linda Goler Blount

Black Women’s Health Imperative

Haibing Lu

Santa Clara University - Information Systems and Analytics

Michael A. Santoro

Santa Clara University

Date Written: January 22, 2021

Abstract

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes 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 into or right after overbooked slots disproportionately to the general population.

Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities.

Methodology: 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.

Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack 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.

Managerial implications: 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.

Keywords: Appointment Scheduling, Machine Learning, Algorithmic Bias, Socio-economic Bias, Racial Bias

Suggested Citation

Samorani, Michele and Harris, Shannon and Blount, Linda Goler and Lu, Haibing and Santoro, Michael A., Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling (January 22, 2021). Samorani, M., Harris, S.L., Blount, L.G., Lu, H. and Santoro, M.A., 2021. Overbooked and overlooked: machine learning and racial bias in medical appointment scheduling. Manufacturing & Service Operations Management., Available at SSRN: https://ssrn.com/abstract=3467047 or http://dx.doi.org/10.2139/ssrn.3467047

Michele Samorani (Contact Author)

Santa Clara University - Information Systems and Analytics ( email )

500, El Camino Real
Santa Clara, CA 95053-0382
United States

Shannon Harris

Virginia Commonwealth University (VCU) ( email )

1015 Floyd Avenue
Richmond, VA 23284
United States

Linda Goler Blount

Black Women’s Health Imperative ( email )

700 Pennsylvania Ave, SE
Ste. 2059
Washington, DC 2003
United States

Haibing Lu

Santa Clara University - Information Systems and Analytics ( email )

500, El Camino Real
Santa Clara, CA 95053-0382
United States

Michael A. Santoro

Santa Clara University ( email )

Leavey School of Business
500 El Camino Real
Santa Clara, CA 95050
United States
408-5516001 (Phone)

HOME PAGE: http://www.michaelAsantoro.com

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