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.

基于预期疾病组的付款是许多国家/地区使用的报销规则。原来是医院的支付规则暗示的。这种付款的秘密是费用付款以及医院的活动付款。一致认为这种支付规则最不可能被参与者操纵。但是，每组规定的费用取决于记录的信息，然后使用复杂的算法处理这些信息。如果数据本身可以被操纵怎么办？结果将是基于操纵因素的每组费用，这将导致医院之间的预算分配效率低下。使用具有 1.45 亿次住宿的独特法国纵向数据库，我明确地证明了更精细分类的实施导致了升级学习效果。最终结果是将预算从公立非研究性医院转移到营利性医院。2009 年的政策导致升级与护理生产趋势的任何变化脱节。