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Racial bias in a medical algorithm favors white patients over sicker black patients
Related: Dissecting racial bias in an algorithm used to manage the health of populations (Science)
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Source: Washington Post
Racial bias in a medical algorithm favors white patients over sicker black patients
By Carolyn Y. Johnson
Oct. 24, 2019 at 2:00 p.m. EDT
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. But the same issue almost certainly exists in other tools used by other private companies, nonprofit health systems and government agencies to manage the health care of about 200 million people in the United States each year, the scientists reported in the journal Science.
Correcting the bias would more than double the number of black patients flagged as at risk of complicated medical needs within the health system the researchers studied, and they are already working with Optum on a fix. When the company replicated the analysis on a national data set of 3.7 million patients, they found that black patients who were ranked by the algorithm as equally as in need of extra care as white patients were much sicker: They collectively suffered from 48,772 additional chronic diseases.
“It’s truly inconceivable to me that anyone else’s algorithm doesn’t suffer from this,” said Sendhil Mullainathan, a professor of computation and behavioral science at the University of Chicago Booth School of Business, who oversaw the work. “I’m hopeful that this causes the entire industry to say, ‘Oh, my, we’ve got to fix this.’ ”
The algorithm wasn’t intentionally racist — in fact, it specifically excluded race. Instead, to identify patients who would benefit from more medical support, the algorithm used a seemingly race-blind metric: how much patients would cost the health-care system in the future. But cost isn’t a race-neutral measure of health-care need. Black patients incurred about $1,800 less in medical costs per year than white patients with the same number of chronic conditions; thus the algorithm scored white patients as equally at risk of future health problems as black patients who had many more diseases.
-snip-
By Carolyn Y. Johnson
Oct. 24, 2019 at 2:00 p.m. EDT
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. But the same issue almost certainly exists in other tools used by other private companies, nonprofit health systems and government agencies to manage the health care of about 200 million people in the United States each year, the scientists reported in the journal Science.
Correcting the bias would more than double the number of black patients flagged as at risk of complicated medical needs within the health system the researchers studied, and they are already working with Optum on a fix. When the company replicated the analysis on a national data set of 3.7 million patients, they found that black patients who were ranked by the algorithm as equally as in need of extra care as white patients were much sicker: They collectively suffered from 48,772 additional chronic diseases.
“It’s truly inconceivable to me that anyone else’s algorithm doesn’t suffer from this,” said Sendhil Mullainathan, a professor of computation and behavioral science at the University of Chicago Booth School of Business, who oversaw the work. “I’m hopeful that this causes the entire industry to say, ‘Oh, my, we’ve got to fix this.’ ”
The algorithm wasn’t intentionally racist — in fact, it specifically excluded race. Instead, to identify patients who would benefit from more medical support, the algorithm used a seemingly race-blind metric: how much patients would cost the health-care system in the future. But cost isn’t a race-neutral measure of health-care need. Black patients incurred about $1,800 less in medical costs per year than white patients with the same number of chronic conditions; thus the algorithm scored white patients as equally at risk of future health problems as black patients who had many more diseases.
-snip-
Read more: https://www.washingtonpost.com/health/2019/10/24/racial-bias-medical-algorithm-favors-white-patients-over-sicker-black-patients/
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Source: The Verge
A health care algorithm affecting millions is biased against black patients
A startling example of algorithmic bias
By Colin Lecher @colinlecher Oct 24, 2019, 2:00pm EDT
A health care algorithm makes black patients substantially less likely than their white counterparts to receive important medical treatment. The major flaw affects millions of patients, and was just revealed in research published this week in the journal Science.
The study does not name the makers of the algorithm, but Ziad Obermeyer, an acting associate professor at the University of California, Berkeley, who worked on the study says “almost every large health care system” is using it, as well as institutions like insurers. Similar algorithms are produced by several different companies as well. “This is a systematic feature of the way pretty much everyone in the space approaches this problem,” he says.
The algorithm is used by health care providers to screen patients for “high-risk care management” intervention. Under this system, patients who have especially complex medical needs are automatically flagged by the algorithm. Once selected, they may receive additional care resources, like more attention from doctors. As the researchers note, the system is widely used around the United States, and for good reason. Extra benefits like dedicated nurses and more primary care appointments are costly for health care providers. The algorithm is used to predict which patients will benefit the most from extra assistance, allowing providers to focus their limited time and resources where they are most needed.
To make that prediction, the algorithm relies on data about how much it costs a care provider to treat a patient. In theory, this could act as a substitute for how sick a patient is. But by studying a dataset of patients, the authors of the Science study show that, because of unequal access to health care, black patients have much less spent on them for treatments than similarly sick white patients. The algorithm doesn’t account for this discrepancy, leading to a startlingly large racial bias against treatment for the black patients.
-snip-
A startling example of algorithmic bias
By Colin Lecher @colinlecher Oct 24, 2019, 2:00pm EDT
A health care algorithm makes black patients substantially less likely than their white counterparts to receive important medical treatment. The major flaw affects millions of patients, and was just revealed in research published this week in the journal Science.
The study does not name the makers of the algorithm, but Ziad Obermeyer, an acting associate professor at the University of California, Berkeley, who worked on the study says “almost every large health care system” is using it, as well as institutions like insurers. Similar algorithms are produced by several different companies as well. “This is a systematic feature of the way pretty much everyone in the space approaches this problem,” he says.
The algorithm is used by health care providers to screen patients for “high-risk care management” intervention. Under this system, patients who have especially complex medical needs are automatically flagged by the algorithm. Once selected, they may receive additional care resources, like more attention from doctors. As the researchers note, the system is widely used around the United States, and for good reason. Extra benefits like dedicated nurses and more primary care appointments are costly for health care providers. The algorithm is used to predict which patients will benefit the most from extra assistance, allowing providers to focus their limited time and resources where they are most needed.
To make that prediction, the algorithm relies on data about how much it costs a care provider to treat a patient. In theory, this could act as a substitute for how sick a patient is. But by studying a dataset of patients, the authors of the Science study show that, because of unequal access to health care, black patients have much less spent on them for treatments than similarly sick white patients. The algorithm doesn’t account for this discrepancy, leading to a startlingly large racial bias against treatment for the black patients.
-snip-
Read more: https://www.theverge.com/2019/10/24/20929337/care-algorithm-study-race-bias-health
