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Public Health Rep. 2001 Jan-Feb; 116(1): 45–50.
PMCID: PMC1497292
Patients with diagnosed diabetes mellitus can be accurately identified in an Indian Health Service patient registration database.
C. Wilson, L. Susan, A. Lynch, R. Saria, and D. Peterson
Diabetes Center of Excellence, Phoenix Indian Medical Center, Phoenix, AZ 85016, USA.
C. Wilson: charlton.wilson/at/pimc.ihs.gov
Abstract
OBJECTIVE: The computerized patient registration databases maintained by the Indian Health Service (IHS) represent a potentially important source of data about the epidemic of diabetes among American Indian and Alaskan Native people. The purpose of this study is to determine the accuracy of this data source, and to identify the optimal search criteria to identify patients with a diagnosis of diabetes in an IHS patient registration database. METHODS: The authors compared the results of a series of computerized searches to a "gold standard" sample of 465 manually reviewed charts from a large IHS facility. RESULTS: Among patients ages 15 years and older, the best criterion for identifying patients diagnosed with diabetes was the presence of at least one purpose of visit narrative identified by a 250.00 to 250.93 ICD-9 code. The presence of a single computerized code for diabetes identified patients with diagnosed diabetes with a sensitivity of 92% (95% confidence interval [CI] 81, 97), a specificity of 99% (95% CI 98, 99), and a calculated positive predictive value of 94% (95% CI 85, 99). In a separate chart review of 462 charts of patients who had at least one 250.00 to 250.93 ICD-9 code recorded in the database, 435 had a diagnosis of diabetes for an observed positive predictive value of 94%. Because the prevalence of diabetes varies by age of the patient, the positive predictive value of the ability to identify patients with diabetes also varies by age. CONCLUSION: A computerized search of an IHS patient database can identify patients with a diagnosis of diabetes with an accuracy that is similar to the reported accuracy from other health care system databases.
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