Geneviève Cadieux1, Robyn Tamblyn. 1. Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A1A3, Canada. genevieve.cadieux@mail.mcgill.ca
Abstract
OBJECTIVE: To assess the accuracy of physician billing claims for identifying acute respiratory infections in primary care. STUDY SETTING. Nine primary care physician practices in Montreal, Canada (2002-2005). STUDY DESIGN: A validation study was carried out to compare diagnoses in 3,526 physician billing claims with diagnoses documented in the corresponding patient medical records. DATA COLLECTION: In-office medical record abstraction. PRINCIPAL FINDINGS: Claims had a high positive predictive value (PPV), negative predictive value, and specificity for identifying respiratory infections; however, their sensitivity was below 50 percent. Large variation in sensitivity and PPV was observed among physicians. CONCLUSIONS: Because claims data are now routinely used to monitor antibiotic prescribing in primary care, future research should determine if acute respiratory infection diagnoses are missing from claims at random, or if bias is present.
OBJECTIVE: To assess the accuracy of physician billing claims for identifying acute respiratory infections in primary care. STUDY SETTING. Nine primary care physician practices in Montreal, Canada (2002-2005). STUDY DESIGN: A validation study was carried out to compare diagnoses in 3,526 physician billing claims with diagnoses documented in the corresponding patient medical records. DATA COLLECTION: In-office medical record abstraction. PRINCIPAL FINDINGS: Claims had a high positive predictive value (PPV), negative predictive value, and specificity for identifying respiratory infections; however, their sensitivity was below 50 percent. Large variation in sensitivity and PPV was observed among physicians. CONCLUSIONS: Because claims data are now routinely used to monitor antibiotic prescribing in primary care, future research should determine if acute respiratory infection diagnoses are missing from claims at random, or if bias is present.
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