Intervening on the Data to Improve the Performance of Health Plan Payment Methods
The conventional method for developing health care plan payment systems uses existing data to study alternative algorithms with the purpose of creating incentives for an efficient and fair health care system. In this paper, we take a different approach and modify the input data rather than the algorithm, so that the data used for calibration reflect the desired levels of spending rather than the observed spending levels typically used for setting health plan payments. We refer to our proposed method as “intervening on the data.” We first present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our approach in two applications in Medicare: an inefficiency example focused on underprovision of care for individuals with chronic illnesses, and an unfairness example addressing health care disparities by geographic income levels. We empirically compare intervening on the data to two other methods commonly used to address inefficiencies and disparities: adding risk adjustor variables, and introducing constraints on the risk adjustment coefficients to redirect revenues. Adding risk adjustors, while the most common policy approach, is the least effective method in our applications. Intervening on the data and constrained regression are both effective. The “side effects” of these approaches, though generally positive, vary according to the empirical context. Intervening on the data is an easy-to-use, intuitive approach for addressing economic efficiency and fairness misallocations in individual health insurance markets.
This research was supported by the National Institute of Aging (P01 AG032952) and the Laura and John Arnold Foundation. Bergquist received additional support from the Harvard Data Science Initiative. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Timothy J. Layton
Disclosures, Timothy Layton
No funder or other agency had the opportunity to review this research prior to publication. Potentially relevant professional and financial relationships in the past 3 years:
1. NIMH Postdoctoral Fellowship [T32-019733] (salary)
2. Harvard Medical School: Assistant Professor (salary)
3. Litigation consulting with Greylock MacKinnon and Associates (consulting fees $30-40k)
4. Consulting fees from University of Texas – Austin for project “Selection Incentives in US Health Plan Design.” [funded by Pfizer] ($10k)
5. Grant from Laura and John Arnold Foundation. “Risk Adjustment Re-design.” Co-investigator. (12% time)
6. Grant from NIMH. “Mental Health Coverage and Payment in Private Health Plans.” [R01-MH094290] Co-investigator. (20% time)