Risk adjustment improvements

The Centers for Medicare and Medicaid Services (CMS) recently published new guidance on a new iteration of their Accountable Care Organization (ACO) initiatives for Medicare.  One of the most intriguing things is how they are thinking about the interaction of reinsurance/stop-loss policies and risk adjustment.  They are making a big and needed leap forward.

P. 36

Starting with PY2023, rather than protecting against exposure for high cost beneficiaries whose healthcare spending exceeds a fixed attachment point (as was the case of PY2021-PY2022), the optional stop-loss arrangement will instead protect against exposure for high cost beneficiaries whose healthcare spending exceeds their predicted spending by a certain amount (attachment point). This approach is known as “residual based reinsurance”. Predicted spending for a beneficiary will be determined by the ACO’s benchmark and the beneficiary’s risk score, using either the CMS-HCC prospective risk adjustment model for Standard ACOs and New Entrant ACOs or the CMMI-HCC concurrent risk adjustment model for High Need Population ACOs.

CMS will calculate the model-wide stop-loss attachment points prospectively, prior to the start of each Performance Year, based on expenditure data derived from a national reference population of Medicare FFS beneficiaries. These model-wide attachment points will be adjusted to the beneficiary level (generating an attachment point for each beneficiary) using beneficiary risk scores and the ACO’s regionally based benchmarks

This is complex. Let me break this down.

We know prospectively that some people have medical conditions that will likely make them wicked expensive to treat.  That is fine. The ACOs are given the predicted increase in extra spending that a particularly sick individual will receive.  The bump payment is based on regression analysis that attempts to estimate the incremental cost of a given cluster of diagnosis codes and demographics.  The bump payment is an average payment for a particular subgroup.  The members of a subgroup will vary in costs.  Sometimes the variance is pretty tight. Sometimes the variance is widely divergent.

Currently stop-loss applies when ACOs have patients who are very high costs.  Some of these patients have anticipated very high costs because they have serious diseases like multiple myeloma.  Some of these very high cost patients have a stretch of bad luck and become acutely ill during the year and require unexpected intensive care stays, expensive drugs and prolonged rehab.  Right now stop loss pays insurers once someone reaches a spending threshold whether or not that spending level was anticipated. Sometimes it is.  Sometimes it is not.

The new policy would change stop loss so that it only triggers when people hit unexpectedly high levels of spending that the ACO is not already compensated for in their baseline payments.  This is a big deal.   It changes the incentives for insurers to think in terms of risk adjusted expected costs and revenue instead of total costs and revenue.  It will change selection and screening strategies as some patients won’t effectively be the source of an insurer double-dip between higher risk adjusted payments and then stop-loss payments even if total spend is below the risk adjusted expectation.

I think that this logic needs to apply to the ACA.  Right now many states run reinsurance waivers with caliper attachment levels.  All of those states also use the CMS risk adjustment program.  This allows for significant double-dipping for individuals with high cost diseases where the insurer already receives a significant risk adjustment transfer as I noted in a comment letter earlier this year:

 

One area that was not mentioned in the white paper is the interaction of risk adjustment and reinsurance waivers.  This is a critical challenge as Section 1332 reinsurance waivers have proliferated since the initial approvals for the 2018 plan year.(32)  Reinsurance has been critical in reducing gross premiums.(33,34)  States have adopted several methods.  Most notably some states have adopted disease specific models where the reinsurance pool pays claims for individuals with certain, pre-specified diseases that are likely to be high cost.  This model reduces right hand tail risk.  Most states that have adopted a reinsurance waiver has instead used a caliper model where the state reinsurance funds are used to pay some percentage of claims between an attachment point and a ceiling.   Both of these models likely cover significant portion of costs that are currently accounted for in the risk adjustment model and this may lead to double counting.

CMS has partially recognized the challenge of double counting with the operation of the national high cost catastrophic reinsurance program where CMS pays a portion of an individual claims in excess of $1,000,000.  CMS has truncated the incremental value of these claims in the calculation of disease category coefficiencts so that insurers are compensated for the portion of risk that they bear rather than the gross risk.

However with state reinsurance programs, there is no state specific models which accounts for the portion of predictable high cost claims that the insurer receives credit for in terms of risk adjustment transfers but also receives funding from the state reinsurance pool.  This problem is likely to be particularly acute in states, like Colorado and Georgia,(35) with multiple reinsurance models operating concurrently over various sub-state geographies.

Policy Recommendation:  CMS should require states that operate 1332 reinsurance programs to adjust disease specific coefficients to minimize double credit of both risk adjustment and reinsurance payments.

 

Programs that are intended to minimize the impact of adverse selection are critical for guaranteed issued insurance programs. The step to align stop loss with residual unexpected expenses will improve incentives and should be more widely adopted.