Timothy J. Layton

Upcoding: Evidence from Medicare on Squishy Risk Adjustment

Upcoding—manipulation of patient diagnoses in order to game payment systems—has gained significant attention following the introduction of risk adjustment into US insurance markets. We provide new evidence that enrollees in private Medicare plans generate 6% to 16% higher diagnosis-based risk scores than they would generate under fee-for-service Medicare, where diagnoses do not affect payments. Our estimates imply upcoding generates billions of dollars in excess public spending annually and significant consumer choice distortions. We show that coding intensity increases with vertical integration, reflecting a principal-agent problem faced by insurers, who desire more intense coding from the physicians with whom they contract.

Risk-Adjustment Simulation: Plans May Have Incentives To Distort Mental Health And Substance Use Coverage

Under the Affordable Care Act, the risk-adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the risk-adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace risk adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D risk adjustment is one potential policy step toward addressing this problem in the Marketplaces.

The Power of Reinsurance in Health Insurance Exchanges to Improve the Fit of the Payment System and Reduce Incentives for Adverse Selection

Risk adjustment and reinsurance protect plans against risk of losses and contend with adverse selection in the new health insurance Exchanges. This article assesses the power of reinsurance in the context of other plan payment features, including prospective and concurrent risk adjustment. Using data from the Medicare Expenditure Panel Survey (MEPS) to draw an “Exchange population,” we simulate the contribution of reinsurance to improving the fit of the payment system to plan costs and to mitigating incentives for adverse selection for groups of enrollees with selected chronic illnesses. Modest reductions in attachment points equate the payment-system fit of retrospective to concurrent risk adjustment. Reinsurance is very powerful in fitting payments to costs and moderately effective in dealing with selection incentives.

Risk Corridors and Reinsurance in Health Insurance Marketplaces: Insurance for Insurers

Health Insurance Marketplaces established by the Affordable Care Act implement reinsurance and risk corridors. Reinsurance limits insurer costs associated with specific individuals, while risk corridors protect against aggregate losses. Both tighten the insurers distribution of expected costs. This paper compares the economic costs and consequences of reinsurance and risk corridors. We simulate the insurers cost distribution under reinsurance and risk corridors using data for a group of individuals likely to enroll in Marketplace plans from the Medical Expenditure Panel Survey. We compare reinsurance and risk corridors in terms of risk reduction and incentives for cost containment. We find that reinsurance and one-sided risk corridors achieve comparable levels of risk reduction for a given level of incentives. We also find that the policies being implemented in the Marketplaces (a mix of reinsurance and two-sided risk corridor policies) substantially limit insurer risk but perform similarly to a simpler stand-alone reinsurance policy.

Higher Incentive Payments in Medicare Advantage's Pay-for-Performance Program Did Not Improve Quality But Did Increase Plan Offerings.

OBJECTIVE To evaluate the effects of the size of financial bonuses on quality of care and the number of plan offerings in the Medicare Advantage Quality Bonus Payment Demonstration. DATA SOURCES Publicly available data from CMS from 2009 to 2014 on Medicare Advantage plan quality ratings, the counties in the service area of each plan, and the benchmarks used to construct plan payments. STUDY DESIGN The Medicare Advantage Quality Bonus Payment Demonstration began in 2012. Under the Demonstration, all Medicare Advantage plans were eligible to receive bonus payments based on plan-level quality scores (star ratings). In some counties, plans were eligible to receive bonus payments that were twice as large as in other counties. We used this variation in incentives to evaluate the effects of bonus size on star ratings and the number of plan offerings in the Demonstration using a differences-in-differences identification strategy. We used matching to create a comparison group of counties that did not receive double bonuses but had similar levels of the preintervention outcomes. PRINCIPAL FINDINGS Results from the difference-in-differences analysis suggest that the receipt of double bonuses was not associated with an increase in star ratings. In the matched sample, the receipt of double bonuses was associated with a statistically insignificant increase of +0.034 (approximately 1 percent) in the average star rating (p > .10, 95 percent CI: -0.015, 0.083). In contrast, the receipt of double bonuses was associated with an increase in the number of plans offered. In the matched sample, the receipt of double bonuses was associated with an overall increase of +0.814 plans (approximately 5.8 percent) (p < .05, 95 percent CI: 0.078, 1.549). We estimate that the double bonuses increased payments by

Measuring efficiency of health plan payment systems in managed competition health insurance markets

3.43 billion over the first 3 years of the Demonstration. CONCLUSIONS At great expense to Medicare, double bonuses in the Medicare Advantage Quality Bonus Payment Demonstration were not associated with improved quality but were associated with more plan offerings.

Computational health economics for identification of unprofitable health care enrollees

Adverse selection in health insurance markets leads to two types of inefficiency. On the demand side, adverse selection leads to plan price distortions resulting in inefficient sorting of consumers across health plans. On the supply side, adverse selection creates incentives for plans to inefficiently distort benefits to attract profitable enrollees. Reinsurance, risk adjustment, and premium categories address these problems. Building on prior research on health plan payment system evaluation, we develop measures of the efficiency consequences of price and benefit distortions under a given payment system. Our measures are based on explicit economic models of insurer behavior under adverse selection, incorporate multiple features of plan payment systems, and can be calculated prior to observing actual insurer and consumer behavior. We illustrate the use of these measures with data from a simulated market for individual health insurance.

Screening in Contract Design: Evidence from the ACA Health Insurance Exchanges

Summary Health 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.

Marketplace Plan Payment Options for Dealing with High-Cost Enrollees

We study insurers’ use of prescription drug formularies to screen consumers in the ACA Health Insurance Exchanges. We begin by showing that Exchange risk adjustment and reinsurance succeed in neutralizing selection incentives for most, but not all, consumer types. A minority of consumers, identifiable by demand for particular classes of prescription drugs, are predictably unprofitable. We then show that contract features relating to these drugs are distorted in a manner consistent with multi-dimensional screening. The empirical findings support a long theoretical literature examining how insurance contracts offered in equilibrium can fail to optimally trade-off risk protection and moral hazard.

Deriving risk adjustment payment weights to maximize efficiency of health insurance markets

Two of the three elements of the ACAs “premium stabilization program,” reinsurance and risk corridors, are set to expire in 2017, leaving risk adjustment alone to protect plans against risk of high-cost cases. This paper considers potential modifications of the HHS risk adjustment methodology to maintain plan protection against risk from high-cost cases within the current regulatory framework. We show analytically that modifications of the transfer formula and of the risk adjustment model itself are mathematically equivalent to a conventional actuarially fair reinsurance policy. Furthermore, closely related modifications of the transfer formula or the risk adjustment model can improve on conventional reinsurance by figuring transfers or estimating risk adjustment model weights recognizing the presence of a reinsurance function. In the empirical section, we estimate risk adjustment models with an updated and selected version of the data used to calibrate the federal payment models, and show, using simulation methods, that proposed modifications improve fit at the person level and protect small insurers against high-cost risk better than conventional reinsurance. We simulate various “attachment points” for the reinsurance equivalent policies and quantify the trade-offs of higher and lower attachment points.