Pay-for-performance schemes: Should optimal prices vary across system and clinical quality indicators?

Authors

  • Sverre Ole Grepperud University of Oslo

DOI:

https://doi.org/10.5617/njhe.5932

Keywords:

pay for performance, multi-level organizations, system and clinical quality.

Abstract

Quality indicators are classified into system or clinical quality indicators. Typically, different levels of an organization steer each of the two types of indicators. Decentralized levels control clinical indicators (blood pressure, blood sugar etc.) while centralized levels control system indicators (waiting time, electronic health records etc.). In this paper we examine optimal pay-for-performance schemes for the two indicators by considering a model consisting of hierarchy of principal-agent interactions where pay-for-performance rewards are distributed to the centralized level (unit of accountability). We find that the optimal pay-for-performance price depends on factors such as the degree and distribution of altruistic preferences, quality costs, the marginal cost of public funds, and the interdependence between the quality variables. The optimal price should differ for system and clinical indicators both when an internal incentive system is in place and when this is not the case. The optimal price for clinical indicators is to reflect the centralized levels’ ability to steer the decentralized level - the type of internal contract that exists between the two levels of the organization. The optimal price for system indicators is independent of the type of internal contract since such indicators are under the control of the unit of accountability. Finally, it is shown that rewarding organizations on the basis of clinical quality indicators can be optimal also when such incentives are not transmitted to the decentralized level of the organization. This conclusion is the result of the indirect effects that non-incentivized variables (system indicators) might have on the incentivized ones (clinical indicators).

Published: Online May 2019.

 

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Published

2019-05-17