Using risk-adjustment models to identify high-cost risks
Using risk-adjustment models to identify high-cost risks
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DOI：
10.1097/01.mlr.0000094480.13057.75
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发表时间：
2003-11-01
期刊：
影响因子：
3
通讯作者：
Bachman, DJ
中科院分区：
文献类型：
--
作者：
Meenan, RT;Goodman, MJ;Bachman, DJ
背景我们研究了各种公开可用的风险模型的能力，以确定高成本的个人和登记组使用多HMO管理数据。在1995-1996年150万个个体水平观察的多HMO管理数据集上估计了五种风险调整模型（全球风险调整模型[EAD]、诊断成本组[DCG]、调整后的临床组[ACG]、Rx风险和优先费用）。模型产生的个人层面的年度费用预测与实际值进行比较的分布。在每一次分配中都设定了预先规定的“高成本”阈值。计算1%和0.5%“高成本”患病率的受试者工作特征曲线下面积（AUC），以及正确识别的“高成本”美元比例。结果基于单独的106，000个观测验证数据集。对于1%和0.5%的“高成本”患病率目标，ACG、DCG、DCG和优先费用在总体歧视方面非常相似（AUC，0.83-0.86）。给定0.5%的患病率目标和0.5%的预测阈值，DCG、ESTA和Prior-expense比其他模型多捕获了963，000美元（约3%）的“高成本”样本美元。DCG在哮喘、糖尿病和抑郁症的登记者中获得了最多的“高成本”美元;人口统计学群体（医疗补助成员、64岁以上成员和13岁以下儿童）的预测性能在不同模型中存在差异。风险模型可以有效地识别哪些登记者可能会产生未来的高成本，哪些人可以从案例管理中受益。最准确的风险模型的预测性能得到改善所带来的美元价值对决策者来说应该是有意义的，并鼓励更广泛地使用这些模型来确定高成本。
BACKGROUND. We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data.METHODS. Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset.MAIN RESULTS. For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models.CONCLUSIONS. Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.