Racial Bias in Hospitals Linked to UnitedHeath Group’s Algorithm

Researchers Find Racial Bias in Hospital Algorithm

By Melanie Evans & Anna Wilde Mathews, The Wall Street Journal, October 25, 2019.

Black patients were less likely than white patients to get extra medical help, despite being sicker, when an algorithm used by a large hospital chose who got the additional attention, according to a new study underscoring the risks as technology gains a foothold in medicine.

Hospitals use the algorithm—from Optum, UnitedHealth Group Inc.’s 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. The reason? The algorithm used cost to rank patients, and researchers found health-care spending for black patients was less than for white patients with similar medical conditions.

“What the algorithm is doing is letting healthier white patients cut in line ahead of sicker black patients,” said Dr. Ziad Obermeyer, the study’s lead author and an acting associate professor of health policy at the University of California, Berkeley. 

Optum advises its customers that its predictive algorithms shouldn’t replace physician judgment, a company spokesman said. Efforts to use analytics in health care have only scratched the surface of their potential and should be continually reviewed and refined, he said.

Optum’s algorithm is used by more than 50 organizations, according the company’s website. Partners Healthcare in Boston is among those to have used it, according to published research. A Partners spokesman said the hospital system is vigilant about how well its algorithms perform. He added a Partners researcher co-authored the paper, which “is an important step in rooting out some of the flaws that exist.”

The Washington Post and Science News earlier reported Optum is the algorithm’s developer. Algorithms, developed by computers crunching vast data sets, are increasingly shaping choices in medicine, from interpreting medical scans to predicting who might become addicted to opioids, suffer a dangerous fall or end up in the hospital.

The technology can speed up and improve some decisions, leading to better treatment for patients, supporters say. But doctors who get suggestions to tweak their patients’ care based on the findings of algorithms often don’t know the details of the technology that led to the recommendation.

Poorly designed algorithms risk reinforcing racial and gender biases, technology experts caution, as studies of algorithms in nonmedical settings like credit scoring, hiring and policing have found.

Algorithms “can give the gloss of being very data-driven, when in fact there are a lot of subjective decisions that go into setting up the problem in the first place,” said Solon Barocas, an assistant professor at Cornell University who is also a principal researcher at Microsoft Research.

Researchers behind the study said well-designed algorithms could help reduce bias that leads to wide disparities in health-care outcomes and access to care. They created an alternative algorithm that increased the percentage of those identified for extra help who were black to about 47%, up from 18%.

“It’s a tool that can do a great deal of good and a great deal of bad, it merely depends on how we use the tool,” said Sendhil Mullainathan, a University of Chicago computational science professor who was an author of the study.

Hospitals and health insurers across the U.S. use the Optum algorithm to spot patients who could benefit from extra help from nurses, pharmacists and case workers, the authors of the study said.

To identify those with the biggest medical needs, the algorithm looks at patients’ medical histories and how much was spent treating them, and then predicts who is likely to have the highest costs in the future.

For the study, data-science researchers looked at the assessments made by one hospital’s use of the algorithm. The study didn’t name the hospital. The researchers focused on the algorithm’s rankings of 6,079 patients who identified themselves as black in the hospital’s records, and 43,539 who identified as white and didn’t identify themselves as any other race or ethnicity.

Then the researchers assessed the health needs of the same set of patients using their medical records, laboratory results and vital signs, and developed a different algorithm.

Using that data, the researchers found that black patients were sicker than white patients who had a similar predicted cost. Among those rated the highest priority by the hospital’s algorithm, black patients had 4.8 chronic diseases compared with 3.8 of the conditions among white patients.

The researchers found the number of black patients eligible for fast-track enrollment in the program more than doubled by prioritizing patients based on their number of chronic conditions, rather than ranking them based on cost.

The findings show “how a seemingly benign choice of label (that is, health cost) initiates a process with potentially life-threatening results,” Ruha Benjamin, author of “Race After Technology” and an associate African-American studies professor at Princeton University, said in an accompanying commentary in Science.

Algorithms are playing an increasing role in medicine, though largely invisible to patients. Doctors are using algorithms to read scans for lung cancer, for instance. Hospitals are deploying the technology to spot which critically ill patients are likely to worsen dramatically. Meantime, health insurers are using algorithms for reasons including to detect patients who are at risk of opioid addiction or who appear headed toward costly lower-back surgery.

Alan Muney, a former executive at health insurer Cigna Corp., said it is common for insurers to use the projected cost of care as a focus in selecting who might get extra outreach or support.

“It’s troubling there was such a big difference” in the effects for black and white patients based on an algorithm focused on cost, he said.

Insurers are developing algorithms that include variables beyond medical costs, including issues that might signal barriers to accessing care, such as financial stress and food insecurity, he said.