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

Abstract

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.

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

“…Risk sharing can take several forms, but usually comes with a trade-off between selection and efficiency [47]. However, recent research has shown that some forms of risk sharing, like residual-based reinsurance, can maintain adequate incentives for efficiency while reducing the incentive for risk selection [48,49]. The reason for this is that residual-based reinsurance compensates for high spending net of the risk-equalization payment and avoids compensating for high spending that is already well compensated for by the risk-equalization model.…”

Changing roles of health insurers in France, Germany, and the Netherlands: any lessons to learn from Bismarckian systems?

“…Moreover, long-term institutional support for this program is uncertain. In addition, insurers have limited incentives to invest in improving care for people with chronic conditions because these are still undercompensated by the system of risk equalisation despite major improvements (Van Kleef and van Vliet, 2022). Hence, health insurers primarily compete on price to attract healthy enrolees, while the main driver for investing in better quality of care is their perceived social mission (Stolper et al, 2019(Stolper et al, , 2022.…”

“…Hence, health insurers primarily compete on price to attract healthy enrolees, while the main driver for investing in better quality of care is their perceived social mission (Stolper et al, 2019(Stolper et al, , 2022. Currently, new methods are being explored (e.g., constrained regression, high-risk pooling, and machine learning) to improve or augment the system of risk equalisation to solve the problem of undercompensating people with (multiple) chronic conditions (Van Kleef and van Vliet, 2022). Finally, the increasing need for integrated care due to the increasing number of people with multiple chronic conditions, reinforced by workforce shortages, requires more collaboration among insurers and providers, which may result in a new balance between competition and cooperation.…”

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

“…Therefore, over the past three decades, research and policy implementations regarding risk equalization design have focused on reducing the predictable profits and losses to diminish the incentives for risk selection (for an overview see [ 7 ]). Although the substantial advancements made to risk equalization models did result in notable improvements in predictive strength, the most sophisticated morbidity-based models currently in place do not eliminate predictable profits and losses [ 18 , 30 ].…”