Dr Justin Bachmann Discusses Consequences of Value-Based Care Being Done Incorrectly

Insufficient risk adjustment is a dangerous consequence of incorrectly implemented value-based care models, explained Justin Bachmann, MD, MPH, FACC, instructor of Medicine and Health Policy at Vanderbilt University Medical Center.

How are value-based care models done incorrectly and what can be the consequences of those flaws?
I think the probably one of the real dangers of measuring value, I guess, inelegantly or perhaps incorrectly is just insufficient risk adjustment. Risk adjustment is never going to be perfect and at the end of the day we have to try to measure value. But if the risk adjustment is wildly imperfect then that can lead to patient selection and also sometimes provider selection.

For example, we know from prior episodes in New York and Pennsylvania in the early '90s, when mortality rates were reported on that were not sufficiently risk adjusted that led to a lot of patient selection. Patients—and this was demonstrated by an economist—who were sicker, providers were selecting patients who were healthier for procedures. Those were the types of change in behavior that you’ll see if there is insufficient risk adjustment.

I think the other thing that really has to be thought about and that people need to be cognizant of is the role of statistical variation in measuring value. Justin Dimick, MD, MPH, a health services researcher, posted a paper in 2006 in JAMA where he demonstrated that on average for a hospital center to be able to detect an increase of the mortality rate twice that of the national benchmark then that facility had to be doing about 270 bypasses and that’s over the course of 1 year. So that’s to detect an increase over the statistical margin of error. You start breaking that down at the provider level and pretty quickly you get within your error bars.

A lot of times when we talk about value-based care we talk about benchmarking, comparing providers to other providers, comparing facilities to other facilities, and there really almost always needs to be some other sort of error bar, some sort of measure so that people are actually cognizant of what is just random variation and what isn’t. If it’s within the error bar, it’s just random variation and one can’t be confidant of those results and they can lead to really, sometimes, dangerous conclusions.
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