WARNING: THIS IS GOING TO BE A VERY GEEKY POST AS I’M TRYING TO FIGURE OUT WHAT I THINK I AM THINKING
I’m thinking that my dissertation is likely to be a Defense Against the Dark Arts dissertation on the ways that insurers and other risk bearing entities respond to incentives that are skewed. Risk adjustment is critical to aligning insurer incentives with societal incentives. I think we have a problem. I need to think some things through.
Claims based risk adjustment is quite common. It is used to move money to insurers that attract and insure individuals with high, expected and plausibly predictable future medical expenses. Some claims based risk adjustment systems are zero sum like the ACA where the money moves from insurers with low coded risk to insurers with high coded risk. Some risk adjustment systems are externally funded like in Medicare Advantage where CMS pays out risk adjustment. Risk adjustment is critical for any guaranteed issue, community rated(ish) products otherwise insurers will only compete on being as unattractive as possible to people with likely high medical expenses while also being attractive to people with few if any probable expenses. This means narrowing of networks, increased gatekeeping, prior authorization and administrative burden run-around for everyone as the game to play is hot potato with short fuse hand grenades.
Claims based risk adjustment requires claims. Claims are a function of utilization. Utilization is a function of actual medical acuity, access to medical care (cost-sharing, transportation, administrative burdens are some potential barriers that vary based on both individual, cultural and geographic basis), and willingess to use medical care. At a given level of acuity some people are more able and willing to go to a clinical environment for care than others for all sorts of reasons. Any given moment of formal utilization is a non-zero chance that a risk adjustable diagnosis is entered into the formal bureaucratic system of accounting and creditation. Any moment of non-formal utilization or outright care avoidance (rub some dirt on it medicine…) is purely a zero probability chance of creating a risk adjustable diagnosis.
Claims based risk adjustment relies on diagnoses. A claim can have 25 diagnoses (Dx) on it. Each Dx slot can either be empty or filled with a Dx Code. Conditional on it being filled, there is some probability that the code is a risk adjustable code and some probability that it is not a risk adjustable code. The number of codes that are filled in is a function of the interaction of the individual patient, the clinician, and the broader, local medical systems. Some patients may be deemed to be more credible in reporting their symptoms that leads to a diagnosis being entered onto a claim while others may not be able to either report at all or have their reports discounted. Some clinicians will code everything and anything, some will only code the immediate problem. Most are somewhere in between. Coding environments like Critical Access Hospitals will have different coding practices than tertiary academic medical centers. The distribution of coding environments is likely not random. It could be associated with various metrics of social vulnerability.
Claims based risk adjustment relies on the accumulation of novel diagnosis groups. Any diagnosis that is risk adjustable only adds incremental value to the risk adjustment transfer if it is unique. The probability that any new diagnosis slot is filled with a unique to the individual/time period dyad diagnosis grouping is a function of everything above plus the number of interactions. An individual with no utilization who then has one visit will have a fairly high probability that they are adding to risk adjustment value. An individual with 100 clinical encounters in the year having one more encounter is very unlikely to have a new diagnosis that has not been coded before on the 101st claim of the year.
If we think about the construct of “Predictable Healthcare Spending” as quasi-latent and each claim is a quasi-random draw into that well, some draws will come up with no risk adjustable diagnosis, a lot of draws for a lot of people will come up with diagnoses that were previously drawn before, and a few draws will come up with new stuff. The new stuff is likely to happen early rather than late for prevalent conditions and quasi-randomly for new conditions. As I’ve been thinking about this, I just keeping on thinking about the Rubber Ducky Game at school carnivals where every rubber duckie has a prize but almost all of them are small prizes but every now and then there is a rubber ducky with a big prize…. risk adjustment is not quite this random as people go to specialists because there is a prior belief that there is something big/weird happening but this is how I’m visualizing the draw process at the moment.
So where am I going with this?
If we think that the probability of a risk adjustment score is a cumulative interaction of the probabilities of an actual problem (or at least a medically defensible codeable problem) plus the probability of a patient actually having at least one clinical encounter plus the probability that a risk adjustable diagnosis is coded plus the probability that the diagnosis grouping is unique for the patient/period dyad then we need to think that these are socially constructed scores. People who are likely, all else equal, to seek care and who are likely to get a risk adjustable diagnosis are probably meaningfully different than people who either are not able/willing to see care or conditional on seeking care, less likely to get a diagnosis that is risk adjustable for the same fundamental condition.
Risk adjustment as an economic and actuarial process moves money to insurers whose population codes as sick. Coding as sick is both a technocratic and a social process. Insurers will want to seek out individuals whose net revenue (premiums + risk adjustment) is greater than expected expenses. If we think that risk adjustment is socially skewed, it overweights people who can get coded “well”/”heavily” and underweights people who either have low utilization or have low probabilities of their underlying medical conditions being coded aggressively.