Specificity and Sensitivity of Claims-Based Algorithms for Identifying Members of Medicare+choice Health Plans That Have Chronic Medical Conditions
Published in: Health Services Research, v. 39, no. 6, pt. 1, Dec. 2004, p. 1839-1860
Posted on RAND.org on January 01, 2004
OBJECTIVE: To examine the effects of varying diagnostic and pharmaceutical criteria on the performance of claims-based algorithms for identifying beneficiaries with hypertension, heart failure, chronic lung disease, arthritis, glaucoma, and diabetes. STUDY SETTING: Secondary 1999-2000 data from two Medicare+Choice health plans. STUDY DESIGN: Retrospective analysis of algorithm specificity and sensitivity. DATA COLLECTION: Physician, facility, and pharmacy claims data were extracted from electronic records for a sample of 3,633 continuously enrolled beneficiaries who responded to an independent survey that included questions about chronic diseases. PRINCIPAL FINDINGS: Compared to an algorithm that required a single medical claim in a one-year period that listed the diagnosis, either requiring that the diagnosis be listed on two separate claims or that the diagnosis to be listed on one claim for a face-to-face encounter with a health care provider significantly increased specificity for the conditions studied by 0.03 to 0.11. Specificity of algorithms was significantly improved by 0.03 to 0.17 when both a medical claim with a diagnosis and a pharmacy claim for a medication commonly used to treat the condition were required. Sensitivity improved significantly by 0.01 to 0.20 when the algorithm relied on a medical claim with a diagnosis or a pharmacy claim, and by 0.05 to 0.17 when two years rather than one year of claims data were analyzed. Algorithms that had specificity more than 0.95 were found for all six conditions. Sensitivity above 0.90 was not achieved all conditions. CONCLUSIONS: Varying claims criteria improved the performance of case-finding algorithms for six chronic conditions. Highly specific, and sometimes sensitive, algorithms for identifying members of health plans with several chronic conditions can be developed using claims data.