Journal Article

Machine Learning and Medical Appointment Scheduling: Creating and Perpetuating Inequalities in Access to Health Care/Comments

Rodenberg, Howard, Blount, Linda Goler, Samorani, Michele

2020

Overview

We are deeply concerned about how machine learning and algorithms create and perpetuate inequalities in health. We are to believe that algorithms are developed to ensure that no one will have an unfair advantage over anyone else and that human bias is removed from decisionmaking. Sounds good in theory.In real-life circumstances- such as medical diagnoses and policies that determine access to health care and social services or where your child is placed in school-algorithms can separate populations into groups of haves and have-nots along racial lines, exacerbating the racial disparity experienced by the different groups. Algorithms can determine the health of entire communities. Invisible to most of us, algorithms are described as the great equalizers.However, unlike people, all algorithms are not created equal. Scheduling a medical appointment is the most common way for patients to access a health provider: a patient asks for an appointment and is given a day and time to see a doctor. If she's on time, she expects that she'll be seen at or about the time of her appointment. Straightforward and fair? Or not? Our recent study1 argues that state-of-theart appointment scheduling algorithms may, in fact, contribute to racial disparities, because they make Black patients wait longer than non-Black patients.In our study, in which we examined electronic scheduling systems in safety net clinics, we revealed how racial bias is woven into the algorithms of electronic health records scheduling systems. To understand how this happens, consider how modern appointment scheduling systems work. To maximize efficiency, most outpatient clinics overbook some of their appointment slots, that is, they give the same appointment time to more than one patient. Overbooking is meant to ensure that providers are fully utilized even if some patients fail to show up for their scheduled appointment. However, ifpatients who are scheduled in overbooked slots do show up, some of them will experience waiting time at the clinic because the provider can see only one patient at a time.

• Publisher: American Public Health Association
• Subject: Acoustics / Algorithms / Artificial intelligence / Bias / Clinics / Computerized medical records / Decision theory / Efficiency / Electronic health records / Electronic medical records / Equalizers / Health care / Health care access / Health disparities / Health records / Health services / Human bias / Inequalities / Learning algorithms / Machine learning / Medical appointments / Medical diagnosis / Medical records / Medicine / Outpatient clinics / Patient satisfaction / Patients / Political appointments / Probability / Public health / Race factors / Racial bias / Racial differences / Racial inequality / Racism / Safety / Scheduling / Social services / Socioeconomic factors / Socioeconomic status / Time