Comparing methods to identify general internal medicine clinic XXXX with @entity712 .

@entity1 @entity712 @entity1289 @entity3362 @entity2514 @entity1405 @entity6838 @entity1002
OBJECTIVES: Identification of @entity1 with @entity1002 is the first step in identifying which @entity1 may benefit from clinical practice guidelines. The purpose of this study was to develop and validate a computerized tool using clinical information that is commonly available to identify @entity1 with @entity1002 ( @entity1002 ). METHODS: We performed a cross-sectional study of @entity1 seen in a Department of Veterans Affairs General Internal Medicine Clinic who had echocardiography or radionuclide ventriculography performed as part of their clinical care. RESULTS: We identified 2246 subjects who had at least one cardiac imaging study. A total of 778 (34.6%) subjects met study criteria for @entity1002 . Subjects with @entity1002 were slightly older than subjects without @entity1002 (70 years vs 68 years, P =.00002) but were similar with regard to sex and race. Subjects with @entity1002 were more likely to have prescriptions for @entity1289 ( @entity1289 ) inhibitors, @entity1405 , digoxin, loop diuretics, @entity2514 , @entity3362 , and angiotensin II receptor antagonists. Of the variables included in the final predictive model, @entity1289 inhibitors, loop diuretics, and @entity6838 exerted the greatest predictive power. Discriminant analysis demonstrated that models containing pharmacy information were consistently more accurate (75% accurate [65% sensitivity, 81% specificity]) than those models that contained only International Classification of Diseases, 9th revision (ICD-9), codes, including ICD-9 codes for @entity712 (72% accurate [80% sensitivity, 68% specificity]). CONCLUSIONS: We demonstrated that an automated, computer-driven algorithm identifying @entity1002 permits simple, rapid, and timely identification of @entity1 with @entity712 by use of only routinely collected data. Future research is needed to develop accurate electronic identification of @entity712 and other common chronic conditions.
