Milcent Carine
Health Economics, Center for National Scientific Research, CNRS - Paris School of Economics - PSE, 48 Bd Jourdan, 75014, Paris, France.
Int J Health Econ Manag. 2021 Mar;21(1):1-26. doi: 10.1007/s10754-020-09287-x. Epub 2020 Oct 31.
A prospective disease group-based payment is a reimbursement rule used in a wide array of countries. It turns to be the hospital's payment rule to imply. The secret of this payment is a fee payment as well as a hospital's activity based payment. There is a consensus to consider this rule of payment as the least likely to be manipulated by the actors. However, the defined fee per group depends on recorded information that is then processed using complex algorithms. What if the data itself can be manipulated? The result would be a fee per group based on manipulated factors that would lead to an inefficient budget allocation between hospitals. Using a unique French longitudinal database with 145 million stays, I unambiguously demonstrate that the implementation of a finer classification led to an upcoding-learning effect. The end result has been a budget transfer from public non-research hospitals to for-profit hospitals. The 2009 policy lead to upcoding disconnected from any changes in the trend of production of care.
基于疾病组的前瞻性支付是许多国家采用的一种报销规则。它成为了医院隐含的支付规则。这种支付方式的奥秘在于它既是一种费用支付，也是一种基于医院活动的支付。人们普遍认为这种支付规则最不容易被相关行为者操纵。然而，每个疾病组的规定费用取决于记录的信息，然后使用复杂算法进行处理。如果数据本身可以被操纵会怎样？结果将是基于被操纵因素的每个疾病组费用，这会导致医院之间预算分配效率低下。利用一个拥有1.45亿住院记录的独特法国纵向数据库，我明确证明了实施更精细的分类导致了编码上调学习效应。最终结果是预算从公立非研究型医院转移到了营利性医院。2009年的政策导致编码上调与医疗服务提供趋势的任何变化脱节。
