School of Economics, University of Surrey, Guildford, UK.
Centre for Health Economics, University of York, York, UK.
Health Econ. 2019 Mar;28(3):387-402. doi: 10.1002/hec.3851. Epub 2018 Dec 27.
Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.
英国家庭精神健康医院的报销制度正在逐步从按项目付费转变为按活动和结果付费。在新模式下，患者被分为 20 个具有相似需求水平的组，称为聚类，可能会为每个聚类预先分配价格。做出聚类决策的临床医生有很大的自由裁量权，原则上可以直接影响医院获得的报销水平。这可能会产生向上编码的激励。临床医生的分配决策得到了一个名为“心理健康聚类工具”的临床聚类算法的支持，该算法提供了一个外部参考，聚类行为可以据此进行基准测试。本研究的目的是调查预测聚类与实际聚类之间的不匹配程度，并测试提供者在聚类行为方面是否存在系统差异。我们使用了英格兰所有在 2014/15 财政年度首次进行聚类的精神健康患者的行政数据，并对聚类过度、不足或匹配的情况进行了多项多水平模型估计。结果表明，医院在匹配错误的概率上存在系统差异，但这种差异与观察到的医院特征并不一致。
