The impact of morbidity trajectories on identifying high-cost cases: using Taiwan's National Health Insurance as an example
The impact of morbidity trajectories on identifying high-cost cases: using Taiwan's National Health Insurance as an example
复制标题
DOI：
10.1093/pubmed/fdt059
复制
发表时间：
2014-06-01
影响因子：
4.4
通讯作者：
Chang, Hsien-Yen
中科院分区：
文献类型：
--
作者：
Chang, Hsien-Yen
将纵向信息纳入风险调整模型被认为是重要的。本研究旨在评估发病率轨迹如何影响风险调整模型在识别高成本的cases. Claims为基础的风险调整，或没有发病率轨迹来自台湾的国家保险系统的3年索赔，被用来预测是一个潜在的高成本用户。本研究以2002年至2005年连续参加全民健康保险的台湾地区国民为研究对象，随机抽取147 892人为研究样本。采用logistic回归模型。基于分裂分析的性能指标包括统计指标（c-统计量、灵敏度和预测阳性值）、模型识别的真实病例比例和预测病例的医疗利用，随着风险调整模型的全面性增加，模型的性能普遍提高。增加轨迹对模型性能的影响随着模型综合性的增加而降低。这种影响在统计指标和预测组的医疗利用方面最为明显，在识别高成本病例时，可能只有在不太全面的风险调整模型中才需要添加发病率轨迹，其贡献来自较高的c-统计量和增加预测组的医疗利用。
Incorporating longitudinal information into risk-adjustment models has been considered important. This study aimed to evaluate how morbidity trajectories impact risk-adjustment models in identifying high-cost cases.Claims-based risk adjusters, with or without morbidity trajectories derived from 3-year claims from Taiwan's National Insurance System, were used to predict being a prospective high-cost user. A random sample of Taiwanese National Health Insurance enrollees continuously enrolled from 2002 to 2005 (n = 147 892) was the study sample. A logistic regression model was employed. The performance measures, based on the split analysis, included statistical indicators (c-statistics, sensitivity and predictive positive value), proportions of true cases identified by models and medical utilization of predicted cases.As the comprehensiveness of risk adjustment models increased, the performance of the models generally increased. The effect of adding trajectories on the model performance decreased as the comprehensiveness of the model increased. Such impact was most apparent in statistical indicators and medical utilization of the predicted groups.In identifying high-cost cases, adding morbidity trajectories might be necessary only for less comprehensive risk adjustment models, and its contributions came from higher c-statistics and increasing medical utilization of predicted groups.