Patients with newly diagnosed carcinoma of the breast: Validation of a claim-based identification algorithm

被引:39
作者
Leung, KM
Hasan, AG
Rees, KS
Parker, RG
Legorreta, AP
机构
[1] Fdn Hlth Syst, Qual Initiat Div, Woodland Hills, CA 91367 USA
[2] Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA USA
[3] NYU, Sch Med, New York, NY USA
[4] Univ Calif Los Angeles, Dept Radiat Oncol, Los Angeles, CA USA
[5] Mt Sinai Sch Med, Dept Community Med, New York, NY USA
关键词
identification; claim-based algorithm; carcinoma of the breast; classification tree; positive predictive value; medical record review;
D O I
10.1016/S0895-4356(98)00143-7
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
The objectives of this study were to validate a claims-based algorithm for identification of patients with newly diagnosed carcinoma of the breast and to optimize the algorithm. Claims data from all females aged 21 years or older who enrolled in a large California health maintenance organization during the study period from October 1, 1994 through March 31, 1996 were analyzed. Medical records of the patients identified through the claims-based algorithm were reviewed to determine whether the patients were correctly identified. The initial algorithm had a positive predictive value of 84% which was similar to the previous study. The percentages of correct identification significantly increased with the patient's age at diagnosis. Other patient demographic characteristics and facility characteristics mere not related to the accuracy of the identification. Using a classification tree procedure and additional information from the false-positive cases, the initial algorithm was modified for improvement. The best-modified algorithm had a positive predictive value of 92% while only 0.5% (4/837) of the true-positive cases were excluded. The results once again demonstrated that patients with newly diagnosed carcinomas of the breast can be identified using claims data. These databases provide an efficient and effective tool for performing health services studies on large patient populations. J CLIN EPIDEMIOL 52;1:57-64, 1999. (C) 1999 Elsevier Science Inc.
引用
收藏
页码:57 / 64
页数:8
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