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

41 Pages Posted: 23 Oct 2019 Last revised: 20 Jun 2020

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: March 24, 2020

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 the patients with the greatest no-show risk belong to the same demographic group, then that demographic group will be scheduled into or right after overbooked slots disproportionately to the general population.

Academic/Practical Relevance: That 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. Waiting time is undesirable because it increases patients’ frustration and dissatisfaction.

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 demonstrate that the state-of-the-art scheduling method that combines machine learning with scheduling optimization causes the black patients in our dataset to wait about 30% longer than non-black patients. To eliminate that disparity, we develop both “race-aware” and “race-unaware” solution methods: the former consider race explicitly when scheduling patients, whereas the latter do not.

Results: Our results suggest that the race-aware methodology is the only one capable of achieving both goals of eliminating racial disparity and obtaining the same schedule quality as that obtained by the state-of-the-art scheduling method. In contrast, the race-unaware methodologies fail to obtain both efficiency and fairness. We validate our findings both on simulated data and real-world data.

Managerial Implications: Our work allows healthcare schedulers to reap the benefit of machine learning without generating undesirable disparities.

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 (March 24, 2020). 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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