By Leslie Small
To improve care for
patients with the most complex health needs, many providers and payers turn to
risk-prediction tools that use an algorithm to determine which patients need
more intense care management. But a recent study, published in the journal Science,
found that one such widely used algorithm exhibits significant racial bias by
assigning black patients the same level of risk as white patients even when
they are far sicker.
This study garnered
significant media attention, and at least one state's regulators launched an
investigation into UnitedHealth Group, whose Optum subsidiary sells Impact Pro,
the data analytics program that researchers studied.
Brian Powers, M.D., one
of the study's authors and a researcher at Brigham and Women's Hospital, says that
"the algorithm did a great job of what it was specifically designed to do,
which was predict future health care costs." The problem is that the
organizations deploying the tool often "use health care costs as a proxy
for health care need," he says, and black patients tend to cost the health
system less because of a "lack of access to care due to structural
inequalities, and a variety of other issues that have been well
documented." So while there is a correlation between high-risk patients and
high health care spending, just looking at expenditures doesn't paint a truly
accurate picture of patients' health care needs.
Rich Caruana, a Microsoft
Corp. senior researcher who studies machine learning in health care, says he
was "not at all surprised" to learn that researchers uncovered hidden
bias in a predictive algorithm.
"Most of what
machine learning is doing is right, but in addition to these things it's doing
really right, roughly 5% of what it's learning are these sort of silly, wrong
things," he continues. "These are known as treatment effects — we're
seeing patients' risk as more or less based on the treatment that they receive."
From Health Plan Weekly
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