Health Plan Payment in the Netherlands

Kleef, Richard C. van; Eijkenaar, Frank; Vliet, René C.J.A. van; Ven, Wynand P.M.M. van de

doi:10.1016/b978-0-12-811325-7.00014-2

Cited by 25 publications

(55 citation statements)

References 15 publications

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“…For a more comprehensive overview, see Van Kleef et al [29] and Enthoven and Van de Ven [7]. Since this paper investigates unpriced risk heterogeneity under the Dutch risk equalization model of 2016, the following description focuses on the situation of 2016.…”

Section: The Dutch Health Insurance Marketmentioning

confidence: 99%

“…On the one hand, premium-rate restrictions, standardization of the benefits package and open enrollment introduce or increase unpriced risk heterogeneity. On the other hand, risk 1 3 equalization reduces unpriced risk heterogeneity by compensating insurers for predictable variation in medical spending [12,14,19,29]. This paper analyzes unpriced risk heterogeneity in the Dutch health insurance market.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, risk selection may reduce cross-subsidization from low-risk to high-risk individuals when these risk types are concentrated in different health plans (e.g., high-versus lower-quality plans, see previous example). Incomplete cross-subsidization might lead to compromised accessibility and affordability and violates the level playing field for insurers [19,25,26,29].…”

Section: Introductionmentioning

confidence: 99%

“…Take, for instance, the risk adjuster 'pharmacy-based cost groups' (PCGs), which classifies individuals in morbidity groups based on the use of prescribed drugs (related to chronic illness) in the prior year. For most PCGs, individuals must have used at least 181 defined daily doses (DDD) in order to be classified in a relevant PCG [29]. On the one hand, PCGs might not identify all people with a particular chronic condition because some of these people might have used less than 181 DDDs in the prior year.…”

Section: Introductionmentioning

confidence: 99%

“…The same pattern can be observed for other subgroups. So, despite marked improvements of the Dutch model, some unpriced risk heterogeneity remains [24,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Examining unpriced risk heterogeneity in the Dutch health insurance market

2018

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A major challenge in regulated health insurance markets is to mitigate risk selection potential. Risk selection can occur in the presence of unpriced risk heterogeneity, which refers to predictable variation in health care spending not reflected in either premiums by insurers or risk equalization payments. This paper examines unpriced risk heterogeneity within risk groups distinguished by the sophisticated Dutch risk equalization model of 2016. Our strategy is to combine the administrative dataset used for estimation of the risk equalization model (n = 16.9 million) with information derived from a large health survey (n = 387k). The survey information allows for explaining and predicting residual spending of the risk equalization model. Based on the predicted residual spending, two metrics are used to indicate unpriced risk heterogeneity at the individual level and at the level of certain (risk) groups: the correlation coefficient between residual spending and predicted residual spending, and the mean absolute value of predicted residual spending. The analyses yield three main findings: (1) the health survey information is able to explain some residual spending of the risk equalization model, (2) unpriced risk heterogeneity exists both in morbidity and in non-morbidity groups, and (3) unpriced risk heterogeneity increases with predicted spending by the risk equalization model. These findings imply that the sophisticated Dutch risk equalization model does not completely remove unpriced risk heterogeneity. Further improvement of the model should focus on broadening and refining the current set of morbidity-based risk adjusters.

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Section: The Dutch Health Insurance Marketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The same pattern can be observed for other subgroups. So, despite marked improvements of the Dutch model, some unpriced risk heterogeneity remains [24,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Examining unpriced risk heterogeneity in the Dutch health insurance market

2018

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How to deal with persistently low/high spenders in health plan payment systems?

Kleef

Vliet

2022

Health Economics

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Health insurance markets with community‐rated premiums typically include risk adjustment (RA) to mitigate selection problems. Over the past decades, RA systems have evolved from simple demographic models to sophisticated morbidity‐based models. Even the most sophisticated models, however, tend to overcompensate people with persistently low spending and undercompensate those with persistently high spending. This paper compares three methods that exploit spending‐level persistence for improving health plan payment systems: (1) implementation of spending‐based risk adjustors, (2) implementation of high‐risk pooling for people with multiple‐year high spending, and (3) indirect use of spending persistence via constrained regression. Based on incentive measures for risk selection and cost control, we conclude that a combination of the last two options can substantially outperform the first, which is currently used in the health plan payment system in the Netherlands.

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Comparing risk adjustment estimation methods under data availability constraints

et al. 2022

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The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk‐adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk‐adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R2 and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The advantage of ML algorithms is greater in the coarse granularity and fair/rich range of variables set and limited with fine granularity scenarios. The inclusion of detailed morbidity‐ and pharmacy‐based adjustors generally increases fit, although the trade‐off of creating adverse economic incentives must be considered.

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Health Plan Payment in the Netherlands

Cited by 25 publications

References 15 publications

Examining unpriced risk heterogeneity in the Dutch health insurance market

Examining unpriced risk heterogeneity in the Dutch health insurance market

How to deal with persistently low/high spenders in health plan payment systems?

Comparing risk adjustment estimation methods under data availability constraints

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