Risk adjustment is critical to the functionality of health insurance markets with guaranteed issued and community rating. These markets include the ACA individual market, Medicare Advantage, Medicaid managed care and provider based alternative payment models like the Accountable Care Organizations (ACOs) that are increasingly common. Risk adjustment moves money to insurers that are covering a sicker population that is expected to use a lot of services and cost a lot of money. Bad risk adjustment or no risk adjustment means that insurers are primarily competing on their ability to identify and avoid people who are likely to be expensive. Good enough risk adjustment means that insurers are competing on service and ability to control costs while being either risk agnostic or risk seeking.
Most risk adjustment systems rely on claims. Claims have diagnosis and service codes that are entered by the treating clinician that describe what is happening with a patient at a point in time. There is an underlying dynamic that for risk adjustment to work, there have to be claims and for claims to be legit, there has to be people going to the doctor and hospital.
We know that people go to the doctors at different rates. Individuals with really good insurance with broad networks and low cost sharing are more likely to go to the doctor than individuals with high cost-sharing insurance and narrow networks. We know that people who are used to navigating complex bureaucratic systems can navigate the medical system better than people who are disenfranchised, marginalized and who face significant administrative burden and barriers to care.
If we need claims to fuel risk adjustment and the probability of a claim for a given condition is a function of a person’s health, socio-economic status, power, insurance characteristics, and embedded community dynamics, then we should expect, all else being equal, for risk adjustment to be a bit heavy on claims from high status individuals and a bit light on claims from low status individuals who face a lot of barriers to care so that they don’t present as often as the path to getting to the doctor’s office has a lot of potholes, detours and dead ends.
And this is an assumption that we might make in “normal-ish” times.
Yesterday, I was at a seminar where the presented showed us some descriptive and not yet published data. They were looking at a large claims universe pre and post COVID. Much like the work led by Paul Shafer that I was on where we looked at North Carolina Medicaid enrollment by Social Vulnerability Index(SVI) pre and post-COVID, these researchers also looked at SVI. They looked at how SVI interacted with post-COVID utilization stratified by a bunch of reasonable demographic cuts. (I’m trying to be useful but vague to respect their publication probabilities)
Unsurprisingly, utilization for everyone in their sample dropped dramatically in April 2020. They then looked at how service utilization bounced back by SVI and demographic cuts. Controlling for a bunch of reasonable covariates, they found that individuals from low SVI areas had much lower and slower service bounce backs than people from high SVI areas.
This is fascinating on multiple levels. The area that I immediately dug into was the disparities of risk adjustment. We’re putting into a system that is already somewhat skewed to better connected individuals an even larger skew because the baseline trend differences in service utilization by SVI and thus claims generation by SVI is even more skewed by connectiveness and privilege in the post-COVID utilization universe.
So what does this mean (besides a potential dissertation question for me as suggested by one of the department’s senior professors)?
IF my inkling is right, then risk adjustment that uses anything from 2020 (and potentially 2021) will be more variant from actual spending with larger residuals than previous year risk adjustment models. Larger residuals between what individuals with a certain coded profile deliver an insurer in revenue through the combination of premium and risk adjustment flows and what they actually cost increases the incentives for insurers to find ways to cherry pick. Insurers will more aggressively screen for people who have a big risk adjustment number but relatively low costs. Insurers will more aggressively try to find ways to be ugly to people who have small risk adjustment numbers but comparatively high costs.
Getting these market incentives right is, in the best of times, a big challenge. Usually we should aim for good enough. However, assuming the data at the seminar is correct and my inkling that diagnosis codes collected are also skewed because of utilization disparities that were exacerbated by COVID, the incentives won’t be as good as we’ve gotten used to over the past couple of years.
Have you done any posts on Long Term Care Insurance? If so, could you point to some links? If not, could you do a post on LTC at some point?
@Keithly: I’ve done very little on LTC. I will write on that this December once classes are over.
Thanks! That would be very helpful.
Your posts may not elicit many comments. The subject matter is not easy for ongoing comments. But they are useful and important. Thank you.
You said that they controlled “for a bunch of reasonable covariates.” What is considered reasonable? And what would be considered not reasonable to not include?
I’m only a doctor who sees a specific slice of patients so I have my own biases, but I’d like to hear what other people consider reasonable and necessary.
@Eric: Age, previous year HCC score, gender, rurality of county, race/ethnicity, imputed income, estimated household size etc.