Are claims data accurate enough to identify patients for performance measures or quality improvement? The case of diabetes, heart disease, and depression
Are claims data accurate enough to identify patients for performance measures or quality improvement? The case of diabetes, heart disease, and depression
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DOI：
10.1177/1062860606288243
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发表时间：
2006-07-01
影响因子：
1.4
通讯作者：
O'Connor, Patrick J.
中科院分区：
文献类型：
--
作者：
Solberg, Leif I.;Engebretson, Karen I.;O'Connor, Patrick J.
本研究的目的是展示一种方法，可以根据索赔数据准确识别患有特定病症的患者，以改善护理或绩效衡量。在试验病例定义的迭代过程中，随后对 10 至 20 个糖尿病、心脏病或新近治疗的抑郁症病例的重复随机样本进行审查，根据健康计划成员的索赔文件创建了最终的识别算法。最终样本用于计算阳性预测值 (PPV)。当每年仅根据 1 个《国际疾病分类》第九次修订版代码识别病例时，每种疾病的 PPV 都低得令人无法接受（0.20、0.60 和 0.65）。要求 12 个月内 2 个门诊代码或 1 个住院代码（加上考虑糖尿病的药物数据和抑郁症的额外标准）导致 PPV 分别为 0.97、0.95 和 0.95。对于那些想要使用管理数据进行案例识别以进行绩效衡量或质量改进的人来说，这种方法是可行且必要的。
The objective of this study was to demonstrate a method to accurately identify patients with specific conditions from claims data for care improvement or performance measurement. In an iterative process of trial case definitions followed by review of repeated random samples of 10 to 20 cases for diabetes, heart disease, or newly treated depression, a final identification algorithm was created from claims files of health plan members. A final sample was used to calculate the positive predictive value (PPV). Each condition had unacceptably low PPVs (0.20, 0.60, and 0.65) when cases were identified on the basis of only 1 International Classification of Diseases, ninth revision, code per year. Requiring 2 outpatient codes or 1 inpatient code within 12 months (plus consideration of medication data for diabetes and extra criteria for depression) resulted in PPVs of 0.97, 0.95, and 0.95. This approach is feasible and necessary for those wanting to use administrative data for case identification for performance measurement or quality improvement.