Risk adjustment of mental health and substance abuse payments.

Risk adjustment of mental health and substance abuse payments.
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心理健康和药物滥用付款的风险调整。

DOI:
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发表时间:
1998
期刊:
Inquiry : a journal of medical care organization, provision and financing
影响因子:
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通讯作者:
Elizabeth H. Notman
Elizabeth H. Notman
中科院分区:
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文献类型:
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作者:
S. Ettner;R. Frank;T. Mcguire;J. Newhouse;Elizabeth H. Notman

文献摘要

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这项研究利用1992年和1993年私人雇主的数据,比较了不同风险调整方法在预测非老年保险人群的精神健康和药物滥用支出方面的表现。所考虑的方法包括基本人口学模型、动态护理组、改进的动态诊断组和分级共存条件(诊断成本组的修改),以及本文开发的针对精神疾病的独特特征进行风险调整的模型(“共病”模型)。我们主要关注的是无法解释的系统性风险及其与健康计划因参保人选择而经历非凡利润或亏损的可能性的关系。我们使用了一个由两部分组成的模型来估计心理健康和药物滥用支出。我们检查了与每个风险调整系统相关的R2和平均绝对预测误差。我们还检查了为我们数据库中的两个雇主提供服务的健康计划将产生的利润和损失,这是基于自然地选择这些计划的参保人。改进的动态诊断组和共病模型的表现略好于其他模型,但没有一个模型的R2值达到0.10以上。此外,基于实际计划选择的模拟表明,没有一种风险调整方法在计划之间重新分配足够的付款,以补偿系统选择。
This study used 1992 and 1993 data from private employers to compare the performance of various risk adjustment methods in predicting the mental health and substance abuse expenditures of a nonelderly insured population. The methods considered included a basic demographic model, Ambulatory Care Groups, modified Ambulatory Diagnostic Groups and Hierarchical Coexisting Conditions (a modification of Diagnostic Cost Groups), as well as a model developed in this paper to tailor risk adjustment to the unique characteristics of psychiatric disorders (the "comorbidity" model). Our primary concern was the amount of unexplained systematic risk and its relationship to the likelihood of a health plan experiencing extraordinary profits or losses stemming from enrollee selection. We used a two-part model to estimate mental health and substance abuse spending. We examined the R2 and mean absolute prediction error associated with each risk adjustment system. We also examined the profits and losses that would be incurred by the health plans serving two of the employers in our database, based on the naturally occurring selection of enrollees into these plans. The modified Ambulatory Diagnostic Groups and comorbidity model performed somewhat better than the others, but none of the models achieved R2 values above .10. Furthermore, simulations based on actual plan choices suggested that none of the risk adjustment methods reallocated payments across plans sufficiently to compensate for systematic selection.