The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, 35 Centerra Parkway, Suite 110, Lebanon, NH 03766, USA.
Health Care Manag Sci. 2010 Mar;13(1):65-73. doi: 10.1007/s10729-009-9112-0.
Case mix methods such as diagnosis related groups have become a basis of payment for inpatient hospitalizations in many countries. Specifying cost weight values for case mix system payment has important consequences; recent evidence suggests case mix cost weight inaccuracies influence the supply of some hospital-based services. To begin to address the question of case mix cost weight accuracy, this paper is motivated by the objective of improving the accuracy of cost weight values due to inaccurate or incomplete comorbidity data. The methods are suitable to case mix methods that incorporate disease severity or comorbidity adjustments. The methods are based on the availability of detailed clinical and cost information linked at the patient level and leverage recent results from clinical data audits. A Bayesian framework is used to synthesize clinical data audit information regarding misclassification probabilities into cost weight value calculations. The models are implemented through Markov chain Monte Carlo methods. An example used to demonstrate the methods finds that inaccurate comorbidity data affects cost weight values by biasing cost weight values (and payments) downward. The implications for hospital payments are discussed and the generalizability of the approach is explored.
病例组合方法（如诊断相关分组）已成为许多国家住院患者支付的基础。为病例组合系统支付指定成本权重值具有重要意义；最近的证据表明，病例组合成本权重不准确会影响某些基于医院的服务供应。为了开始解决病例组合成本权重准确性的问题，本文的目的是提高由于合并症数据不准确或不完整而导致的成本权重值的准确性。这些方法适用于包含疾病严重程度或合并症调整的病例组合方法。这些方法基于详细的临床和成本信息的可用性，并利用最近的临床数据审计结果。使用贝叶斯框架将临床数据审计信息中关于分类错误概率的信息综合到成本权重值的计算中。模型通过马尔可夫链蒙特卡罗方法实现。用于演示方法的示例发现，不准确的合并症数据会通过向下偏差成本权重值（和付款）来影响成本权重值。讨论了对医院付款的影响，并探讨了该方法的可推广性。
