Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway St, Baltimore, MD 21205, USA.
BMC Med. 2010 Jan 18;8:7. doi: 10.1186/1741-7015-8-7.
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.
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 R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level.
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.
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.
由于其对支付、高危预测建模和提供者绩效评估的影响，基于诊断的风险调整在全球范围内变得越来越重要。台湾的全民健康保险（NHI）计划提供全民覆盖，并维护一个单一的全国计算机化索赔数据库，这使得基于诊断的风险调整得以应用。然而，关于风险调整的研究是有限的。本研究旨在使用来自台湾 NHI 计划的基于索赔的诊断信息，检验调整后的临床群组（ACG）病例组合系统的性能。
随机选择 NHI 参保人样本。那些在 2002 年连续参保的人被纳入同期分析（n=173234），而那些在 2002 年和 2003 年都参保的人被纳入前瞻性分析（n=164562）。2002 年诊断得出的健康状况指标被用于解释 2002 年和 2003 年的健康支出。在比较了七种不同统计模型的性能后，采用了多元线性回归模型。为了避免过度拟合，进行了分割验证。性能指标为个体水平的五种支出类型的调整 R2 和平均绝对预测误差，以及群体水平的总支出预测比。
在解释资源利用方面，更全面的模型表现更好。同期/前瞻性分析中总支出的调整 R2 在人口统计学模型中为 4.2%/4.4%，在 ACG 或 ADG（聚合诊断组）模型中为 15%/10%，在包含 EDCs（扩展诊断聚类）的模型中为 40%/22%。当根据支出五分位数预测群体支出时，所有模型都低估了最高支出群体，高估了其他四个群体。对于基于发病负担的群体，ACG 模型总体表现最好。
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相对于仅基于人口统计学的模型具有更强的解释能力，台湾政府应考虑将基于索赔的模型用于与政策相关的应用。台湾的 ACG 病例组合系统的性能与其他国家的发现相当。这表明，即使 ACG 系统最初是在美国开发的，也可以将其应用于台湾的 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学有关。
