Sharma Anurag
Centre for Health Economics, Monash University, Clayton 3800, Victoria, Australia.
Health Care Manag Sci. 2009 Mar;12(1):38-55. doi: 10.1007/s10729-008-9078-3.
This paper empirically investigates the resource distribution dynamics across Diagnosis Related Groups (DRGs) of elective surgery patients, in a continuing Prospective Payment System (PPS). Existing 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 the distribution of this decline across DRGs, in a PPS. The present paper helps fill this gap. It 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 for inlier episodes suggest that resource redistribution will increase capacity and expected number of admissions for DRGs having increasing waiting times. In addition, adjustments in relative cost weights are perceived as price signals by hospitals leading to a change in their casemix. The results for high outlier patients reveal that improved quality of care is one of the factors causing reduction in high outlier episodes.
本文实证研究了在持续的按病种付费系统（PPS）中，择期手术患者各诊断相关分组（DRG）间的资源分配动态情况。现有的计量经济学文献主要关注按病种付费对平均住院时长（LOS）的影响，并得出结论：实施按病种付费后平均住院时长有所下降。然而，关于在按病种付费系统中这种下降在各诊断相关分组间的分布情况，相关文献较少。本文有助于填补这一空白。它对各诊断相关分组住院时长的经验分布随时间的演变进行了建模。经验分布是使用基于马尔可夫链理论的非参数“随机核方法”进行估计的。对于正常病例的结果表明，资源重新分配将提高等待时间增加的诊断相关分组的容量和预期入院人数。此外，相对成本权重的调整被医院视为价格信号，从而导致其病例组合发生变化。对于高异常值患者的结果显示，护理质量的提高是导致高异常值病例减少的因素之一。
