Incentives For Risk Selection Research Articles

A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients' health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information

BackgroundDespite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness.MethodsWe study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated– though imperfect– risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a ‘high risk pool’ using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers’ incentives for risk selection and cost control, and compare this to the situation without HRP.ResultsThe results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending.ConclusionOur main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Unravelling risk selection in Spanish general government employee mutual funds: evidence from cancer hospitalizations in the public health network

Government employees in Spain are covered by public Mutual Funds that purchase a uniform basket of benefits, equal to the ones served to the general population, from private companies. Companies apply as private bidders for a fixed per capita premium hardly adjusted by age. Our hypothesis is that this premium does not cover risks, and companies have incentives for risk selection, which are more visible in high-cost patients. We focus on a particularly costly disease, cancer, whose prevalence is similar among government employees and the general population. We compare hospitalisations in the public hospitals of the government employees that have chosen public provision and the general population. We analysed a database of hospital discharges in the Valencian Community from 2010 to 2015 (3 million episodes). Using exact matching and logistic models, we find significant risk selection; thus, in hospitalised government employees, the likelihood for a solid metastatic carcinoma and non-metastatic cancer to appear in the registry is 31% higher than in the general population. Lymphoma shows the highest odds ratio of 2.64. We found quantitatively important effects. This research provides indirect evidence of risk selection within Spanish Mutual Funds for government employees, prompting action to reduce incentives for such a practice. More research is needed to figure out if what we have observed with cancer patients occurs in other conditions.

Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison.

In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

Heteroscedasticity of residual spending after risk equalization: a potential source of selection incentives in health insurance markets with premium regulation

Many community-rated health insurance markets include risk equalization (also known as risk adjustment) to mitigate risk selection incentives for competing insurers. Empirical evaluations of risk equalization typically quantify selection incentives through predictable profits and losses net of risk equalization for various groups of consumers (e.g. the healthy versus the chronically ill). The underlying assumption is that absence of predictable profits and losses implies absence of selection incentives. This paper questions this assumption. We show that even when risk equalization perfectly compensates insurers for predictable differences in mean spending between groups, selection incentives are likely to remain. The reason is that the uncertainty about residual spending (i.e., spending net of risk equalization) differs across groups, e.g., the risk of substantial losses is larger for the chronically ill than for the healthy. In a risk-rated market, insurers are likely to charge a higher profit mark-up (to cover uncertainty in residual spending) and a higher safety mark-up (to cover the risk of large losses) to chronically ill than to healthy individuals. When such differentiation is not allowed, insurers face incentives to select in favor of the healthy. Although the exact size of these selection incentives depends on contextual factors, our empirical simulations indicate they can be non-trivial. Our findings suggest that – in addition to the equalization of differences in mean spending between the healthy and the chronically ill – policy measures might be needed to diminish (or compensate insurers for) heteroscedasticity of residual spending across groups.

Predictable profits and losses in a health insurance market with risk equalization: A multiple-contract period perspective

Many social health insurance systems rely on ‘regulated competition’ among insurers to improve efficiency. In the presence of community-rated premiums, risk equalization is an important regulatory feature to mitigate risk-selection incentives in such systems. Empirical studies evaluating selection incentives have typically quantified group-level (un)profitability for one contract period. However, due to switching barriers, a multiple contract period perspective might be more relevant. In this paper, using data from a large health survey (N≈380k) we identify subgroups of chronically ill and healthy individuals in year t and follow these groups over three consecutive years. Using administrative data covering the entire Dutch population (N≈17m), we then simulate the mean per person predictable profits and losses (i.e. spending predicted by a sophisticated risk-equalization model minus actual spending) of these groups over the three follow-up years. We find that most of the groups of chronically ill are persistently unprofitable on average, while the healthy group is persistently profitable. This implies that selection incentives might be stronger than initially thought, underscoring the necessity of eliminating predictable profits and losses for the adequate functioning of competitive social health insurance markets.

Selection Incentives in the Dutch Basic Health Insurance: To What Extent Does End-of-Life Spending Contribute to Predictable Profits and Losses for Selective Groups?

Existing risk-equalization models in individual health insurance markets with premium-rate restrictions do not completely compensate insurers for predictable profits/losses, confronting insurers with risk selection incentives. To guide further improvement of risk-equalization models, it is important to obtain insight into the drivers of remaining predictable profits/losses. This article studies a specific potential driver: end-of-life spending (defined here as spending in the last 1–5 years of life). Using administrative (N = 16.9 m) and health survey (N = 384 k) data from the Netherlands, we examine the extent to which end-of-life spending contributes to predictable profits/losses for selective groups. We do so by simulating the predictable profits/losses for these groups with and without end-of-life spending while correcting for the overall spending difference between these two situations. Our main finding is that—even under a sophisticated risk-equalization model—end-of-life spending can contribute to predictable losses for specific chronic conditions.

The goal of risk equalization in regulated competitive health insurance markets

Different opinions exist about the goal of risk equalization in regulated competitive health insurance markets. There seems to be consensus that an element of the goal of risk equalization is ‘to remove the predictable over- and undercompensations of subgroups of insured’ or, equivalently, ‘to achieve a level playing field for each risk composition of an insurer’s portfolio’ or, equivalently, ‘to remove the incentives for risk selection’. However, the role of efficiency appears to be a major issue: should efficiency also be an element of the goal of risk equalization, or should it be a restriction to the goal, or should efficiency not be an element of the goal or a restriction to the goal? If efficiency plays a role, a comprehensive analysis of the total effect of risk equalization on efficiency needs to be done. An improvement of the performance of a risk equalization scheme has both negative and positive effects on efficiency. Negative effects include the reduction in efficiency via cost- or utilization-based risk adjusters. Positive effects result from leveling the playing field and reducing the incentives for risk selection, which increase efficiency as the outcome of a competitive market. In practice many regulators and policy makers take efficiency into consideration by looking at the negative effects, but hardly at the positive effects. The definition of the goal of risk equalization has consequences for the design and evaluation of risk equalization schemes and for the equalization payments. We describe relevant potential goals, tradeoffs and possible solutions.

Do health insurers use target marketing as a tool for risk selection? Evidence from the Netherlands

In health care systems based on managed competition, enrolees can choose between insurers who are positioned as prudent buyers of care on their behalf. To avoid risk selection, insurers are compensated through a system of risk equalisation. The Dutch system of risk equalisation is generally considered to be one of the most sophisticated in the world. Empirical evidence, however, shows there are still consumer segments that are profitable for insurers. To examine whether insurers use target marketing for attracting these segments, we assessed promotional material used by Dutch insurers during the switching season of 2019. Our findings provide preliminary evidence that large insurers with different brands primarily use their sub brands as strategic vehicles to improve their competitive positions by targeting these brands at financially favourable groups and price sensitive buyers. By contrast, the more visible main brands are targeted at a much broader spectrum of consumer groups to display the insurer's social character. Only a minority of insurers’ marketing expressions are targeted at actual users of care. Despite continuous improvements in the risk equalisation system, on average this group is still unprofitable for insurers. From a health policy perspective, further improvements are key to motivate health insurers to target their efforts at improving care for the chronically ill and to eliminate incentives for risk selection.

Savings or Selection? Initial Spending Reductions in the Medicare Shared Savings Program and Considerations for Reform.

The Medicare Shared Savings Program (MSSP) establishes incentives for participating accountable care organizations (ACOs) to lower spending for their attributed fee-for-service Medicare patients. Turnover in ACO physicians and patient panels has raised concerns that ACOs may be earning shared-savings bonuses by selecting lower-risk patients or providers with lower-risk panels. We conducted three sets of analyses of Medicare claims data. First, we estimated overall MSSP savings through 2015 using a difference-in-differences approach and methods that eliminated selection bias from ACO program exit or changes in the practices or physicians included in ACO contracts. We then checked for residual risk selection at the patient level. Second, we reestimated savings with methods that address undetected risk selection but could introduce bias from other sources. These included patient fixed effects, baseline or prospective assignment, and area-level MSSP exposure to hold patient populations constant. Third, we tested for changes in provider composition or provider billing that may have contributed to bonuses, even if they were eliminated as sources of bias in the evaluation analyses. MSSP participation was associated with modest and increasing annual gross savings in the 2012-2013 entry cohorts of ACOs that reached $139 to $302 per patient by 2015. Savings in the 2014 entry cohort were small and not statistically significant. Robustness checks revealed no evidence of residual risk selection. Alternative methods to address risk selection produced results that were substantively consistent with our primary analysis but varied somewhat and were more sensitive to adjustment for patient characteristics, suggesting the introduction of bias from within-patient changes in time-varying characteristics. We found no evidence of ACO manipulation of provider composition or billing to inflate savings. Finally, larger savings for physician group ACOs were robust to consideration of differential changes in organizational structure among non-ACO providers (eg, from consolidation). Participation in the original MSSP program was associated with modest savings and not with favorable risk selection. These findings suggest an opportunity to build on early progress. Understanding the effect of new opportunities and incentives for risk selection in the revamped MSSP will be important for guiding future program reforms.

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

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit ‘risk adjuster’ in risk equalization models. This study examines an alternative way to exploit this information, namely through ‘constrained regression’ (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.

Diagnosis-based Cost Groups in the Dutch Risk-equalization Model: Effects of Clustering Diagnoses and of Allowing Patients to be Classified into Multiple Risk-classes.

The risk-equalization (RE) model in the Dutch health insurance market has evolved to a sophisticated model containing direct proxies for health. However, it still has important imperfections, leaving incentives for risk selection. This paper focuses on refining an important health-based risk-adjuster in this model: the diagnosis-based costs groups (DCGs). The current (2017) DCGs are calibrated on "old" data of 2011/2012, are mutually exclusive, and are essentially clusters of about 200 diagnosis-groups ("dxgroups"). Hospital claims data (2013), administrative data (2014) on costs and risk-characteristics for the entire Dutch population (N≈16.9 million), and health survey data (2012, N≈387,000) are used. The survey data are used to identify subgroups of individuals in poor or in good health. The claims and administrative data are used to develop alternative DCG-modalities to examine the impact on individual-level and group-level fit of recalibrating the DCGs based on new data, of allowing patients to be classified in multiple DCGs, and of refraining from clustering. Recalibrating the DCGs and allowing enrolees to be classified into multiple DCGs lead to nontrivial improvements in individual-level and group-level fit (especially for cancer patients and people with comorbid conditions). The improvement resulting from refraining from clustering does not seem to justify the increase in model complexity this would entail. The performance of the sophisticated Dutch RE-model can be improved by allowing classification in multiple (clustered) DCGs and using new data. Irrespective of the modality used, however, various subgroups remain significantly undercompensated. Further improvement of the RE-model merits high priority.

Improving risk equalization for individuals with persistently high costs: Experiences from the Netherlands

BackgroundRisk-equalization (RE) models in competitive health insurance markets have become increasingly sophisticated. However, these models still have important imperfections. A specific problem in the Netherlands is that insurers are insufficiently compensated for individuals who can persistently be found in the right-end tail of the cost distribution. ObjectivesThe goal of this study is to explore and evaluate options for improving compensation for persistently high-cost individuals in the Dutch basic health insurance. MethodsPrescription drugs claims (2012) and administrative data on costs and risk-characteristics (2010–2013) for the entire Dutch population are used to identify high-cost individuals and evaluate improvement options. These options – including new risk-classes and a form of risk-sharing – are evaluated in terms of insurers’ incentives for risk-selection and efficiency. ResultsThree significantly undercompensated high-cost groups are identified: users of specific expensive drugs for rare diseases, hemophilia-patients, and individuals whose costs are in the top-0.50% in 3 prior years. The improvement options effectively remove the undercompensations for these groups and lead to a considerable improvement in individual-level model fit. However, the options differ in terms of their (potential) effects on insurers’ efficiency incentives. ConclusionsAlthough this study provides useful insights in the possibilities for improving RE-models for persistently high-cost individuals, improving compensation remains challenging and dependent on the ongoing debate regarding coverage and reimbursement of expensive drugs.

Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N=~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts.

Incentive for Regional Risk Selection in the German Risk Structure Compensation Scheme

The introduction of the new law GKV-FQWG strengthens the competition between statutory health insurance. If incentives for risk selection exist, they may force a battle for cheap customers. This study aims to document and discuss incentives for regional risk selection in the German risk structure compensation scheme. Identify regional autocorrelation with Moran's l on financial parameters of the risk structure compensation schema. Incentives for regional risk selection do indeed exist. The risk structure compensation schema reduces 91% of the effect and helps to reduce risk selection. Nevertheless, a connection between regional situation and competition could be shown (correlation: 69.5%). Only the integration of regional control variables into the risk compensation eliminates regional autocorrelation. The actual risk structure compensation is leading to regional inequalities and as a consequence to risk selection and distortion in competition.

Improving risk equalization with constrained regression

State-of-the-art risk equalization models undercompensate some risk groups and overcompensate others, leaving systematic incentives for risk selection. A natural approach to reducing the under- or overcompensation for a particular group is enriching the risk equalization model with risk adjustor variables that indicate membership in that group. For some groups, however, appropriate risk adjustor variables may not (yet) be available. For these situations, this paper proposes an alternative approach to reducing under- or overcompensation: constraining the estimated coefficients of the risk equalization model such that the under- or overcompensation for a group of interest equals a fixed amount. We show that, compared to ordinary least-squares, constrained regressions can reduce under/overcompensation for some groups but increase under/overcompensation for others. In order to quantify this trade-off two fundamental questions need to be answered: “Which groups are relevant in terms of risk selection actions?” and “What is the relative importance of under- and overcompensation for these groups?” By making assumptions on these aspects we empirically evaluate a particular set of constraints using individual-level data from the Netherlands (N = 16.5 million). We find that the benefits of introducing constraints in terms of reduced under/overcompensations for some groups can be worth the costs in terms of increased under/overcompensations for others. Constrained regressions add a tool for developing risk equalization models that can improve the overall economic performance of health plan payment schemes.

Variation In Accountable Care Organization Spending And Sensitivity To Risk Adjustment: Implications For Benchmarking.

Spending targets (or benchmarks) for accountable care organizations (ACOs) participating in the Medicare Shared Savings Program must be set carefully to encourage program participation while achieving fiscal goals and minimizing unintended consequences, such as penalizing ACOs for serving sicker patients. Recently proposed regulatory changes include measures to make benchmarks more similar for ACOs in the same area with different historical spending levels. We found that ACOs vary widely in how their spending levels compare with those of other local providers after standard case-mix adjustments. Additionally adjusting for survey measures of patient health meaningfully reduced the variation in differences between ACO spending and local average fee-for-service spending, but substantial variation remained, which suggests that differences in care efficiency between ACOs and local non-ACO providers vary widely. Accordingly, measures to equilibrate benchmarks between high- and low-spending ACOs--such as setting benchmarks to risk-adjusted average fee-for-service spending in an area--should be implemented gradually to maintain participation by ACOs with high spending. Use of survey information also could help mitigate perverse incentives for risk selection and upcoding and limit unintended consequences of new benchmarking methodologies for ACOs serving sicker patients.

High cost pool or high cost groups—How to handle high(est) cost cases in a risk adjustment mechanism?

Competitive social health insurance systems (at least) in Western Europe have implemented systems of morbidity based risk adjustment to set a level playing field for insurers. However, many high cost insured still are heavily underfunded despite risk adjustment, leaving incentives for risk selection. In most of these health care systems, there is an ongoing debate about how to deal with such underpaid high cost cases. This study develops four distinct concepts by adding variables to risk adjustment or by setting up a high cost pool for underpaid insured besides the risk adjustment system. Their features, incentives and distributional effects are discussed. With a data set of 6 million insured, performance is demonstrated for Germany. All models achieve a substantial improvement in model fit, measured in terms of R2 as well as CPM. As the results of the various models are different in different dimensions, the trade-offs that have to be dealt with and should be addressed, when implementing a model to reduce underfunding of high cost cases.

Much ado about nothing? The financial impact of physician-owned specialty hospitals.

The U.S. hospital industry has recently witnessed a number of policy changes aimed at aligning hospital payments to costs and these can be traced to significant concerns regarding selection of profitable patients and procedures by physician-owned specialty hospitals. The policy responses to specialty hospitals have alternated between payment system reforms and outright moratoriums on hospital operations including one in the recently enacted Affordable Care Act. A key issue is whether physician-owned specialty hospitals pose financial strain on the larger group of general hospitals through cream-skimming of profitable patients, yet there is no study that conducts a systematic analysis relating such selection behavior by physician-owners to financial impacts within hospital markets. The current paper takes into account heterogeneity in specialty hospital behavior and finds some evidence of their adverse impact on profit margins of competitor hospitals, especially for-profit hospitals. There is also some evidence of hospital consolidation in response to competitive pressures by specialty hospitals. Overall, these findings underline the importance of the payment reforms aimed at correcting distortions in the reimbursement system that generate incentives for risk-selection among providers groups. The identification techniques will also inform empirical analysis on future data testing the efficacy of these payment reforms.

Why 'Optimal' Payment for Healthcare Providers Can Never be Optimal Under Community Rating.

This article extends the existing literature on optimal provider payment by accounting for consumer heterogeneity in preferences for health insurance and healthcare. This heterogeneity breaks down the separation of the relationship between providers and the health insurer and the relationship between consumers and the insurer. Both experimental and market evidence for a high degree of heterogeneity are presented. Given heterogeneity, a uniform policy fails to effectively control moral hazard, while incentives for risk selection created by community rating cannot be neutralized through risk adjustment. Consumer heterogeneity spills over into relationships with providers, such that a uniform contract with providers also cannot be optimal. The decisive condition for ensuring optimality of provider payment is to replace community rating (which violates the principle of marginal cost pricing) with risk rating of contributions combined with subsidization targeted at high risks with low incomes.