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Dr Ashish Jha Discusses Risk Adjustment Methods

Risk adjustment has many variables and cannot always level the playing field between hospitals, said Ashish K. Jha, MD, MPH, the K.T. Li Professor of Health Policy at the Harvard T.H. Chan School of Public Health and the director of the Harvard Global Health Institute.


Risk adjustment has many variables and cannot always level the playing field between hospitals, said Ashish K. Jha, MD, MPH, the K.T. Li Professor of Health Policy at the Harvard T.H. Chan School of Public Health and the director of the Harvard Global Health Institute.

Transcript (slightly modified)

The CMS star ratings for hospitals are adjusted for each hospital’s case mix, but how might risk adjustment methods fail to level the playing field?

So case mix, or risk adjustment, is a standard methodology that we use to try to equalize the playing field between organizations that take care of a lot of sick people, and organizations that take care of fewer sick people. And what we know is that our ability to measure case mix, our ability to measure the severity of illness, sort of varies by condition. It varies based on what kind of data you’re using. So when you use claims data, and for instance if you’re looking at readmissions, we know that risk adjustment really is not good at all. Whereas claims data for, risk adjustment for, heart attack deaths actually is pretty good.

So there isn’t a blanket criticism of risk adjustment that somehow it never works or it always works or it’s always bad for certain conditions. It just depends on the measure you’re looking at. And what we know is that a lot of the measures that went into the star ratings, they’re just, we don’t do a good job of risk adjustment on those, and we can tell because when we look at the hospitals that care for the sickest patients, they tend to be some of the worst performers. 

 
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