Deriving Risk Adjustment Payment Weights to Maximize Efficiency of Health Insurance Markets

Abstract

for comments on a previous draft. We gratefully acknowledge the Dutch Ministry of Health and the Association of Health Insurers for providing access to the administrative data. The authors are solely responsible for the analyses and conclusions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Selection in Health Insurance Markets and Its Policy Remedies

“…But it is also important to expand the range of optimal payment policies to be considered. For example, one approach might consider reinsurance programs that compensate plans based on certain key dimensions of after-the-fact realized costs, but it will be important to focus on dimensions that are least susceptible to moral hazard concerns (Geruso and McGuire 2016;Layton, McGuire, and van Kleef 2016). Another policy alternative might seek to compensate health insurance plans based on certain features of the contracts themselves, rather than the imperfect selection signals generated by risk scores.…”

Achieving Mental Health Care Parity Might Require Changes In Payments And Competition

“…Finally, an idea proposed in a working paper using the risk-adjustment system and data from the Netherlands is to “risk adjust to the system you want, not the system you have.” 33 The idea is this: Suppose policy makers believe that not enough funds are devoted to office-based care for certain chronic conditions. Instead of estimating a risk-adjustment model on the system with underspending for certain conditions, before estimating, move the money to where you want it—for example, by increasing spending for the target diagnoses.…”

Incorporating self-reported health measures in risk equalization through constrained regression

“…Starting from a model of insurer and consumer behavior, they showed that the optimal risk equalization coefficients result from CR with constraints for each of the separate services that health plans are able to distort. Layton et al [21] have empirically implemented this approach. A key difference between the present study and the studies mentioned above is that here the information used as a basis for constraints does not come from administrative data that are available for the entire population, but from a health survey that is only available for a sample of the population.…”