基于DIP付费的再入院精准惩罚规则设计

DIP-based precision rules for hospital readmissions reduction program

合理设计再入院惩罚规则,是降低医院再入院率,提高医疗服务质量的重要手段.我国当前推行基于大数据的按病种分值付费(bigdatadiagnosis-interventionpacket,DIP),虽科学考虑我国医疗发展区域性差异较大特征而取得较好成本监管效果,但因其报销患者单次入院费用导致医院降低再入院率动力不足.基于符合我国国情的DIP成本惩罚规则,构建医保机构、医院与患者间博弈模型,探究监管再入院的精准医保支付与惩罚基准设计.研究发现,医院具有强竞争优势时,设立再入院惩罚规则能够改变医院再入院率决策,然而,固定惩罚界限的惩罚规则不一定能激励医院降低再入院率,重度惩罚规则也不一定是最优惩罚方式.依据医院历史再入院率梯度设计惩罚界限的精准惩罚规则能有效激励医院降低再入院率.扩展讨论两医院具有同等竞争优势情形,证明梯度设计惩罚界限的精准惩罚规则仍奏效.

Penalty rules play a pivotal role in inducing hospitals to reduce readmissions and improve service quality.The big data diagnosis-intervention packet(DIP)implemented in China has resulted in more effective regulation of hospital costs by considering regional differences.However,it lacks incentives for hospitals to reduce readmissions,due to the weaker correlation between readmissions and hospital revenue.This study develops a game-theoretical model consisting of a regulator,hospitals and patients to explore precise penalty rules and penalty benchmarks for readmissions based on DIP.The results show that penalty rules influence hospital readmissions in the situation of hospitals with a strong competitive advantage.Nevertheless,fixed standard penalty rules do not always motivate hospitals to reduce readmissions,and stricter penalty rules are not always optimal.The proposed precision rules can effectively reduce readmission rates by using penalty benchmarks based on historical hospital data.Finally,we extend the analysis to a more general scenario in which two hospitals possess the same competitive advantage and demonstrate that precision rules with gradient penalty benchmarks remain a viable option.