Estimating Generic Drug Use with Electronic Health Records Data from a Health Care Delivery System: Implications for...

Generic drug use in the outpatient setting is typically measured with adjudicated pharmacy claims; however, not all delivery systems have access to these data for their clinical populations.

To develop an algorithm to estimate generic drug use in an outpatient setting using electronic health records (EHR) data.

Twenty-five therapeutic classes were chosen with the potential for low generic use that were prescribed to managed care beneficiaries in a health care system in Northern California. An algorithm was developed to estimate generic drug use based on medication names and dispense-as-written requests from electronic prescriptions, as well as information on generic availability at the time the prescriptions were written. The algorithm was used to quantify a generic utilization rate (GUR) across therapeutic classes and was validated by comparing the estimated GUR to the true GUR, using pharmacy claims corresponding to prescriptions in the same patient cohort.

Among managed care beneficiaries, 104,859 prescriptions were identified for drugs in the therapeutic classes of interest with corresponding pharmacy claims. The algorithm estimated a GUR of 73.7% across 25 unique classes. The actual GUR based on pharmacy claims was 73.1%. Sensitivity (97%) and specificity (89%) of the algorithm were high, and total percentage of agreement was 95%.

An algorithm that estimates generic drug use performed well in a population of managed care beneficiaries. Health care delivery systems may apply methods described in this article to quantify generic drug use in their ambulatory populations for quality improvement and research initiatives, particularly when pharmacy claims are unavailable.

This study was funded by a grant from the U.S. Food and Drug Administration in cooperative agreement with the Johns Hopkins School of Medicine and the Palo Alto Medical Foundation Research Institute (1U01FD005267-01). Romanelli has received research grant support from Pfizer and Janssen Scientific Affairs. Authors have no other conflicts to disclose. Romanelli and Segal contributed the study concept and design. Nimbal took the lead in data collection, assisted by Romanelli. All authors were involved with data interpretation and revision of the manuscript. The manuscript was written by Romanelli and Nimbal.