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Modest risk-sharing significantly reduces health plans’ incentives for service distortion

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Abstract

Public payers often use payment mechanisms as a way to improve the efficiency of the healthcare system. One source of inefficiency is service distortion (SD) in which health plans over/underprovide services in order to affect the mix of their enrollees. Using Israeli data, we apply a new measure of SD to show that a mixed payment scheme, with a modest level of cost-sharing, yields a significant improvement over a pure risk-adjustment scheme. This observation implies that even though mixed systems induce overprovision of some services, their benefits far outweigh their costs.

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Notes

  1. Some papers use the term service level selection for what we refer to as service distortion. We use the terms “service distortion” and “incentive for service distortion” interchangeably in most of the paper except in places where the term “(incentives for) selection” seems more appropriate.

  2. We use the terms Managed Care Organization and health plan interchangeably. An MCO is an entity that contracts to provide healthcare to its members who are often referred to as “subscribers” or “enrollees”. In the US, there are several types of MCOs: Health Maintenance Organization (HMO), the Preferred Provider Organization (PPO), and the Point‐of‐Service plan. (Accountable Care Organizations (ACOs) can also be viewed as a type of MCO). MCOs are also common in countries such as the Netherlands, Switzerland and Israel. MCOs have increasingly become the norm in the delivery of healthcare services over the past 15 years, partly due to the rising cost of healthcare. The commonly stated goal of MCOs is to reduce the cost of healthcare by negotiating lower fees for healthcare services, monitoring the types of treatment utilized by subscribers, and reviewing the relative cost effectiveness of treatments.

  3. Under the assumption of profit maximization.

  4. We are using the version of the index developed in Layton et al. [1] rather than the final formula appearing in Layton et al. [24]. The former is based on group-level deviations while the latter is based on individual-level deviations. We chose the group-level formula since we believe it to be a more realistic description of how health plan managers implement their desired policy. We are not aware of any previous research that has looked at how health plan managers actually implement service distortion—a question left for future research.

  5. This is the index of heterogeneity (ψ) developed by Layton et al. [1] and mentioned above.

  6. For a detailed description of the Israeli payment mechanism, see Appendix 1.

  7. For the distribution of the 2009 health survey population by age, gender and place of residence, as well as the distribution of respondents with a chronic illness, disability, depression or anxiety or cancer by age and gender, see “Appendix 2”.

  8. In order to simplify the analysis, we do not include residence in the periphery as an adjuster in the models. However, this should not affect the results since the periphery adjuster only adds a fixed (and relatively small) sum to the payment for all enrollees living in the periphery regardless of age or gender.

  9. Not surprisingly, the top percentile of individuals according to cost accounts for 22% of the total budget; therefore, 25% of the actual expenditure on them is equal to about 5% of the total budget.

  10. In order to keep the total budget identical across all the examined payment mechanisms, the average revenue per individual is normalized to 1 for all of them.

  11. A retrospective payment covering 12.5% of hospitalization costs represents 5% of a health plan’s actual expenditure (since the weight of inpatient services is 0.4).

  12. The Israeli Central Bureau of Statistics calculates the Periphery Index of local authorities (on a scale of 1–10). It is a combination of two components with equal weights: the Potential Accessibility Index and proximity to the Tel Aviv District.

  13. For more details, see Brammli-Greenberg et al. [26].

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Acknowledgements

An earlier version of this paper was presented at the Risk-Adjustment Network Workshop held in October 2016 in Berlin, Germany. The authors are especially grateful to Konstantin Beck, Randall Ellis, Timothy Layton, Thomas McGuire, Amir Shmueli, Wynand Van de Ven, Richard Van Kleef, and other participants in the workshop for their helpful comments and advice. The authors are also grateful to Glied Sherry, Altman Stuart, Freed Gary and Wittenberg Raphael for their constructive suggestions. This study was funded by a Grant provided by the Israel National Institute for Health Policy Research (Grant number 2012/37).

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Authors and Affiliations

  1. Myers-JDC Brookdale Institute and The Hebrew University, Jerusalem, Israel

    Shuli Brammli-Greenberg

  2. The University of Warwick and Faculty of Management, Tel Aviv University, Tel Aviv, Israel

    Jacob Glazer

  3. Myers-JDC Brookdale Institute, Ben-Gurion University, Beer-Sheva, Israel

    Ruth Waitzberg

  4. Technische Universität in Berlin, Berlin, Germany

    Ruth Waitzberg

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  1. Shuli Brammli-Greenberg
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Correspondence to Shuli Brammli-Greenberg.

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Appendices

Appendix 1: The Israeli health plan payment design

The Israeli capitation formula and risk-adjustment

In 2015, the NHI budget was about €9.1 billion (about €1100 per capita). This budget is transferred to the health plans through three main mechanisms: (1) prospective payments based on a capitation formula with three risk adjusters: age, gender and residence in the geographic periphery; (2) prospective payments for patients with specified “severe illnesses”; and (3) supplementary NHI funding via retrospective payments based on performance. The NHI law also allows health plans to charge user fees at points of service for visits to specialists and related medical professionals, purchase of medication and medical aids, and certain diagnostic exams. These copayments represent around 6.5% of the health plan’s income. This section discusses these compensation mechanisms in greater detail.

The capitation formula

The NHI Law specifies two objectives for the capitation formula: (1) to reduce the incentives of the health plans to favor certain types of enrollees; and (2) to distribute resources among the health plans in a way that takes into account the needs of their enrollees, so as to protect health plans that insure a disproportionate number of high-risk enrollees from financial insolvency [28].

Prospective capitation is the main funding source for the health plans. In 2015, it constituted 88% of the total NHI budget; the other 12% consisted of co-payments and payments for individuals with severe illnesses.

The procedure for deriving risk-adjustment rates

The calculation of the risk-adjustment rates in Israel has historically been based on service use rather than expenditure data.

Three main types of services (also called “Major Service Categories”) are used to calculate the rates of use for each capitation group. The 2010 weights for each type of service (Ws) are presented in Table 2.

Table 2 Weights of health service categories as defined by the 2010 Capitation Committee
Full size table

The Israeli capitation rates for each group j are calculated as follows:

$$R_{j} = \mathop \sum \limits_{s} W_{s} \frac{{{\text{Average use of service }}s {\text{ in group}}\, j}}{{{\text{Average use of service }}s {\text{ in the population}}}},$$
(2)

where Ws is the share of the total spending on service category s out of the total medical expenditures on all services funded under the Israeli National Health Insurance Law.

The j groups are defined by age (0–1, 1–4, 5–14, 15–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, 85+) and gender. Place of residence defines two groups: residence in the periphery or elsewhere. “Periphery” includes the locations classified as being in the four lowest clusters of the CBS’s Geographic Periphery Index.Footnote 12 This risk adjuster is not taken into consideration in the risk-adjustment formula itself, but rather adds a fixed weight of 5% for each enrollee living in the “periphery” (currently about 50€ per enrollee per year).

Essentially, we calculated a health plan’s expenditures on individual i (i.e., xi) using the same formula as that used by the government in order to calculate the capitation rates on the basis of individuals’ use of services (2). Thus,

$$x_{i} = \mathop \sum \limits_{s} W_{s} \frac{{ {\text{use of service }}s {\text{ by individual }}i}}{{{\text{Average use of service }}s {\text{ in the population}}}},$$
(3)

where s is service category. While the Israeli capitation calculation includes only publicly funded services, we include all services, whether funded publicly or privately, in the calculation of the predicted model in order to cover all uses in the system.

We add a fourth service category of “other services” to the three used in the Israeli capitation formula, which includes visits to physiotherapists, speech therapists, psychologists, dieticians and occupational therapists, as well as MRIs.

As mentioned above, Ws is the share of the total spending on service category s out of the total medical expenditure on all services funded under the Israeli National Health Insurance Law. We calculated Ws with a fourth service category (“other services”) taken from the 2013 Health Fund Activity Report (Ministry of Health, 2013); it is subtracted from “visits to physicians” (see Table 3).

Table 3 Weights of health services with the addition of “other services”
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Prospective payments for “severe illnesses” and other supplementary payment mechanisms

As will be discussed below, the payments for “severe illnesses” under the NHI Law are prospective and, as such, can be viewed as simply another element of the capitation formula. However, this category has historically had a separate mechanism in Israel and since the payments under this mechanism are calculated differently than the other capitation payments, we treat this category separately in this section.

Health plans are compensated separately for enrollees with five specific illnesses: AIDS, kidney failure, Gauche, hemophilia and thalassemia. In 2015, this compensation constituted 7% of health plan revenue under the NHI. The NHI also allows health plans to charge small co-payments for prescription drugs, visits to specialists, and certain diagnostic exams. Copayments constituted 6.5% of health plan revenues from the NHI in 2015.

Apart from the NHI budget, health plans may receive additional financial support from the government at the end of each year, depending on the extent to which they have met various targets for fiscal responsibility, efficiency and inequality reduction. In 2015, this support totaled about €0.4 billion. This retrospective funding is intended not only to maintain the health plans’ economic stability but also to promote policy goals. Currently, most of it is used to meet expenditure goals and to balance budgets. However, in the past, some of this support was allocated to improving the quality of elderly care in the community, providing flu vaccinations without co-payments for the elderly and children, and reducing the cost of medicines for the +75 age group.Footnote 13

Appendix 2: Distribution of the 2009 health survey population by age, gender and place of residence and distribution of individuals with a chronic illness, disability, depression or anxiety and cancer, by age and gender

Table 4 presents the distribution of the survey population according to three demographic variables: age, gender and area of residence. 10% of the sample are aged 65 and older (5.4% are women and 4.6% are men), 50.6% are women, and 44.5% live in the periphery (30.2% in the North of Israel and 14.3% in the South), proportions that are similar to those in the general population.

Table 4 Distribution of the 2009 Health Survey population by age, gender and area of residence (corresponding to the j groups used to construct the “budget cells”); in percent, N = 27,382
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Table 5 presents the incidence of various health conditions by age group and gender. As expected, it shows that the overall health conditions of the elderly are inferior to those of the young population. Moreover, it appears that the health conditions of elderly women are inferior to those of elderly men (Tables 6, 7, 8, 9, 10).

Table 5 Distribution of individuals with chronic illness, disability, depression or anxiety and cancer, by age and gender, in percent
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Table 6 Multivariate analysis results of logistic regression; includes both publicly and privately funded services
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Table 7 Multivariate analysis (generalized linear model (GLM) regression): predicted visits to a physician per year
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Table 8 Multivariate analysis (generalized linear model (GLM) regression): predicted hospitalizations per year
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Table 9 Multivariate analysis (generalized linear model (GLM) regression): predicted number of conditions treated with prescription drugs per year
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Table 10 Multivariate analysis (generalized linear model (GLM) regression): predicted visits to other related medical professionals per year
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Appendix 3: Actual use rates by service category, gender and age

In Fig. 4, we present the actual use rates by service category for men and women by age. The four service categories used here are also used to define the budget cells in Eq. (1).

Fig. 4
figure 4

Actual use rates by service category, for men and women by age

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In Table 11, we present ANOVA significance F test results for actual use variables (in order to compare means by gender between age groups). Figure 4 and Table 11 show that the rates for outpatient services are significantly higher for women than for men in the 15–54 age group. Figure 4 shows that there is a steep rise in the rate of inpatient care starting from age 45; however, there is no significant difference between men and women in actual use rates.

Table 11 Analysis of variance (ANOVA) significance F tests for use of service by gender in each age group
Full size table

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Brammli-Greenberg, S., Glazer, J. & Waitzberg, R. Modest risk-sharing significantly reduces health plans’ incentives for service distortion. Eur J Health Econ 20, 1359–1374 (2019). https://doi.org/10.1007/s10198-019-01102-w

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