Risk adjustment has broad general application and is a key part of the Patient Protection and Affordable Care Act (ACA). Yet, little has been written on how data required to support risk adjustment should be collected. This paper offers analytical support for a distributed approach, in which insurers retain possession of claims but pass on summary statistics to the risk adjustment authority as needed It shows that distributed approaches function as well as or better than centralized ones--where insurers submit raw claims data to the risk adjustment authority--in terms of the goals o frisk adjustment. In particular, it shows how distributed data analysis can be used to calibrate risk adjustment models and calculate payments, both in theory and in practice--drawing on the experience of distributed models in other contexts. In addition, it explains how distributed methods support other goals of the ACA, and can support projects requiring data aggregation more generally. It concludes that states should seriously consider distributed methods to implement their risk adjustment programs.
The Patient Protection and Affordable Care Act (ACA) establishes state-based marketplaces, known as "exchanges," where individuals and small employers will be able to purchase health insurance starting in 2014. One potential concern is that there may be adverse selection in the exchange plans. Adverse selection is the allocation of people into plans based on determinants of expected medical spending that plans cannot observe or are prohibited from using to set premiums. The possibility of adverse selection can give purchasers and issuers of insurance incentives to choose or offer plans on the basis of selection rather than cost, quality, or efficiency.
The ACA takes several steps to mitigate problems from adverse selection. Most important, it establishes a risk adjustment program to provide payments to health insurance issuers that cover higher-risk populations financed by payments from issuers that cover lower-risk populations. (1) Given that insurers must cover all applicants on a "guaranteed issue" basis and cannot vary premiums on health status or fully adjust for other factors such as age, risk adjustment functions to balance risk among issuers and make them indifferent as to which individuals they enroll. The goals of the program, as set out by the U.S. Department of Health and Human Services (HHS 2011, [section] 153.330), are as follows:
1. Accurately explain cost variation within a given population;
2. Choose risk factors that are clinically meaningful to providers;
3. Encourage favorable behavior and discourage unfavorable behavior;
4. Limit gaming;
5. Use data that is complete, high in quality, and available in a timely fashion;
6. Provide stable risk scores over time and across plans; and
7. Minimize administrative burden.
As HHS (2011, [section] 153.340) points out, there are three ways to collect the data necessary to support a risk adjustment program:
1. A centralized approach, in which insurers submit raw claims data to HHS;
2. An intermediate approach, in which insurers submit raw claims data to state risk adjustment authorities; and
3. A distributed approach, in which insurers retain possession of raw claims data, but pass on summary statistics to HHS and state authorities as necessary.
Understanding the strengths and weaknesses of these competing approaches is important. Although HHS (2012) has indicated that it would use a distributed approach when operating a risk adjustment program on behalf of a state, it also announced that it was permitting states that elect to operate a risk adjustment program to choose the data collection approach that best suits their needs. Thus when deciding how to implement their programs, states will want to evaluate the likely effects of each approach in terms of the goals previously enumerated. …