An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan
An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan
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DOI：
10.1186/1741-7015-8-7
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发表时间：
2010-01-18
期刊：
影响因子：
9.3
通讯作者：
Weiner, Jonathan P.
中科院分区：
文献类型：
--
作者：
Chang, Hsien-Yen;Weiner, Jonathan P.
背景资料：基于诊断的风险调整正在成为全球的一个重要问题，因为它对支付、高风险预测建模和供应商绩效评估产生影响。台湾全民健康保险方案提供全民保险，并维持一个单一的全国计算机化索赔数据库，从而能够应用基于诊断的风险调整。然而，关于风险调整的研究有限。本研究的目的是研究的性能调整临床组（ACG）的情况下，组合系统使用索赔为基础的诊断信息从台湾NHI programme.Methods：一个随机样本的NHI enrollees被选中。2002年连续入组的患者纳入同期分析（n = 173，234），而2002年和2003年入组的患者纳入前瞻性分析（n = 164，562）。根据2002年诊断得出的健康状况指标被用来解释2002年和2003年的卫生支出。在比较7种不同统计模型的性能后，采用多元线性回归模型。为了避免过拟合，进行了分割验证。结果：综合性较强的模型在解释资源利用方面表现较好，综合性较强的模型在解释资源利用方面表现较好，综合性较强的模型在解释资源利用方面表现较好。在人口统计学模型中，同期/前瞻性分析中总支出的校正R2为4.2%/4.4%，在ACG或ADG（聚合诊断组）模型中为15%/10%，在包含EDC（扩展诊断群）的模型中为40%/22%。在根据支出五分位数预测各组的支出时，所有模型都低估了支出最高的一组，高估了其他四组。对于基于发病负担的群体，ACGs模型总体表现最好。结论：鉴于索赔数据的广泛可用性以及基于索赔的风险调整模型比仅人口统计模型具有上级的解释力，台湾政府应考虑使用基于索赔的模型用于政策相关应用。ACG病例组合系统在台湾的表现与其他国家相当。这表明，尽管ACG系统最初是在美国开发的，但它可以应用于台湾的NHI。本文中的许多发现可能与其他基于诊断的风险调整方法有关。
Background: Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme.Methods: A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R(2) and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level.Results: The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall.Conclusions: Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.