Morning Briefing
Summaries of health policy coverage from major news organizations
Racial Bias In Hospitals: Widely Used Algorithm Favors White Patients Over Sicker Black Patients, Study Finds
The Washington Post: Scientists Detected Racial Bias In A Product Sold By Optum, But The Problem Likely Extends To Algorithms Used By Major Health Systems And Insurers
A widely used algorithm that predicts which patients will benefit from extra medical care dramatically underestimates the health needs of the sickest black patients, amplifying long-standing racial disparities in medicine, researchers have found. The problem was caught in an algorithm sold by a leading health services company, called Optum, to guide care decision-making for millions of people. (Johnson, 10/24)
The Wall Street Journal: Researchers Find Racial Bias In Hospital Algorithm
Hospitals use the algorithm—from Optum, UnitedHealth Group Inc. health-services arm—to find patients with diabetes, heart disease and other chronic ailments who could benefit from having health-care workers monitor their overall health, manage their prescriptions and juggle doctor visits, according to the study published Thursday in the journal Science. Yet the algorithm gave healthier white patients the same ranking as black patients who had one more chronic illness as well as poorer laboratory results and vital signs. (Evans and Wilde Mathews, 10/24)
Los Angeles Times: How Computer Algorithms Help Spread Racial Bias In U.S. Healthcare
“We shouldn’t be blaming the algorithm,” said study leader Dr. Ziad Obermeyer, a machine learning and health researcher at UC Berkeley. “We should be blaming ourselves, because the algorithm is just learning from the data we give it.” An algorithm is a set of instructions that describe how to perform a certain task. A recipe for brownies is an algorithm. So is the list of turns to make to drive to your friend’s party. (Khan, 10/24)
Stat: Widely Used Algorithm In Hospitals Is Racially Biased, Study Finds
The study’s authors then retrained a new algorithm using patients’ biological data, rather than the insurance claims data that the original program used, and found an 84% reduction in bias. Previously, the algorithm was failing to account for a collective nearly 50,000 chronic conditions experienced by black patients. After rejiggering the algorithm, that number dropped to fewer than 8,000. The reduction in bias emphasized what many in the health technology field believe: Algorithms may only be as good as the data behind them. (Chakradhar, 10/24)
