Abstract
SUMMARYHealth insurers may attempt to design their health plans to attract profitable enrollees while deterring unprofitable ones. Such insurers would not be delivering socially efficient levels of care by providing health plans that maximize societal benefit, but rather intentionally distorting plan benefits to avoid high-cost enrollees, potentially to the detriment of health and efficiency. In this work, we focus on a specific component of health plan design at risk for health insurer distortion in the Health Insurance Marketplaces: the prescription drug formulary. We introduce an ensembled machine learning function to determine whether drug utilization variables are predictive of a new measure of enrollee unprofitability we derive, and thus vulnerable to distortions by insurers. Our implementation also contains a unique application-specific variable selection tool. This study demonstrates that super learning is effective in extracting the relevant signal for this prediction problem, and that a small number of drug variables can be used to identify unprofitable enrollees. The results are both encouraging and concerning. While risk adjustment appears to have been reasonably successful at weakening the relationship between therapeutic-class-specific drug utilization and unprofitability, some classes remain predictive of insurer losses. The vulnerable enrollees whose prescription drug regimens include drugs in these classes may need special protection from regulators in health insurance market design.
Highlights
It is widely recognized by economists, health care providers, and policymakers that health insurance markets suffer from adverse selection
We focus on a specific component of health plan design: the prescription drug formulary
Using propensity-score techniques described in previous literature (Layton and others, 2015), we identified the 2 006 216 observations that met our criteria to be representative of the Health Insurance Marketplace population with regard to distributions of age, gender, region, residence in a metropolitan statistical area, inpatient admissions, number of inpatient admissions, and quantile of outpatient and prescription drug spending
Summary
It is widely recognized by economists, health care providers, and policymakers that health insurance markets suffer from adverse selection. A particular type of adverse selection based on consumer behavior is emphasized, where the tendency of sicker consumers to enroll in more comprehensive insurance plans drives up the price of more comprehensive plans and forces healthier consumers out of those plans. This type of benefit distortion is termed service-level selection (Frank and others, 2000; Glazer and McGuire, 2000). Due to the potential for adverse selection problems, many health insurance markets, including the state and federal Health Insurance Marketplaces created by The Patient Protection and Affordable Care Act (ACA), implement a policy known as “risk adjustment”, where insurers with sicker enrollees receive financial transfers from insurers with healthier enrollees. Risk adjustment is far from perfect, potentially leaving unprofitable groups for insurers to identify and avoid (McGuire and others, 2014)
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