Using Electronic Health Records and Linked Claims Data to Assess New Medication Use and Primary Nonadherence in Rheumatology Patients.

Abstract

The objective of this study was to determine the proportion of new medication prescriptions observed in electronic health records (EHR) that represent true incident medication use, accounting for undocumented previous prescriptions (prevalent medication use) and failure to initiate treatment (primary nonadherence) with linked administrative claims data as the reference standard. Using single-specialty rheumatology EHR data from more than 700 community practices in the United States linked to administrative claims data, we identified first (index) EHR prescriptions and assessed the positive predictive value (PPV) of different EHR-derived new user definitions to identify true incident use (no prior claims). We then assessed how often index EHR prescriptions that met a definition of new use resulted in primary nonadherence (no subsequent claims). Overall, 12,405 index EHR prescriptions were identified with PPVs of 0.59 to 0.67 for true incident use. PPVs increased to 0.76 to 0.85 by excluding medications listed during the EHR medication reconciliation process and further increased to 0.87 to 0.93 by requiring ≥12 elapsed months since the first rheumatology office visit. Primary nonadherence at three months was observed in 33% to 38% overall and varied substantially by medication class, ranging from 15% to 23% for conventional synthetic disease-modifying antirheumatic drugs (DMARDs) to 54% to 64% for targeted synthetic DMARDs. New DMARD use was accurately distinguished from prevalent use with EHR prescriptions and simple new user definitions that include current medications collected during medication reconciliation. Primary nonadherence was frequent and varied by DMARD class. This has important implications for epidemiologic studies using EHR data and for optimal delivery of clinical care.