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Inter-DRG Resource Allocation in a Prospective Payment System: A Stochastic Kernel Approach

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  • Anurag Sharma

Abstract

This paper empirically investigates the distribution dynamics of resource allocation decisions across Diagnosis Related Groups (DRGs), in a continuing Prospective Payment System (PPS) . The theoretical literature suggests a PPS could lead to moral hazard effects, where hospitals have an incentive to change the intensity of services provided to a given set of patients, a selection effect whereby hospitals have an incentive to change the severity of patients they see, and thirdly hospitals could change their market share by specialization (practice style effect). The related econometric literature has mainly focussed on the impact of PPS on average Length of Stay (LOS) concluding that the average LOS has declined post PPS. There is little literature on distribution of this decline across DRGs, in a PPS. The present paper helps fill this gap. The paper models the evolution over time of the empirical distribution of LOS across DRGs. The empirical distributions are estimated using a non parametric “stochastic kernel approach” based on Markov Chain theory. The results suggest that relative prices of DRGs are one of the determinants in resource allocation across DRGs. In addition, a reduction in the high outlier episodes indicates existence of potential selection effect even in a continuing PPS.

Suggested Citation

  • Anurag Sharma, 2007. "Inter-DRG Resource Allocation in a Prospective Payment System: A Stochastic Kernel Approach," Health, Econometrics and Data Group (HEDG) Working Papers 07/21, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:07/21
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    Keywords

    Keywords: Resource Allocation; Stochastic Kernel; Case-mix funding; Prospective Payment System; Length of Stay;

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