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Using Aggregated Pharmacy Claims to Identify Primary Nonadherence
Dominique Comer, PharmD, MS; Joseph Couto, PharmD, MBA; Ruth Aguiar, BA; Pan Wu, PhD; and Daniel Elliott, MD, MSCE

Using Aggregated Pharmacy Claims to Identify Primary Nonadherence

Dominique Comer, PharmD, MS; Joseph Couto, PharmD, MBA; Ruth Aguiar, BA; Pan Wu, PhD; and Daniel Elliott, MD, MSCE
We used aggregated pharmacy claims data available within the electronic health record to identify a high rate of primary nonadherence in a nonintegrated primary care network.

The results of our study need to be taken in the context of its limitations. First, only 791 of 3284 patients who were prescribed an antihypertensive met our study definition. This was largely due to the fact that the pharmacy fill history was not available because the provider had not accessed the medication history after the new prescription. Second, indication is not attached to e-prescriptions. Although we only studied medications with a primary indication of hypertension, it is possible that some antihypertensives in our data set were also used for additional indications. Next, the completeness of our data is uncertain, as prescriptions paid for with cash or coupons, or those filled by pharmacies or pharmacy benefit managers who do not contribute to our source database, may not be available. We did find, however, that patients with more discrepancies at baseline were less likely to have evidence of a fill. This may indicate missing data, but it could also be that nonadherence to baseline medications at the time of an index prescription is associated with future nonadherence. However, the consistency of our rate of primary nonadherence with previous literature is reassuring. Finally, we do not have information regarding co-payments or out-of-pocket costs, which are known to be associated with nonadherence.9

Despite these limitations, our findings suggest that aggregated pharmacy claims available within a provider EHR may be useful in identifying patients with primary nonadherence in routine clinical practice. However, if interventions are meant to impact clinical care, the data must be sufficiently complete, accurate, and accessible in real time to clinicians with minimal interruptions to work flow. Ideally, identification and data sharing could be automated and presented to providers in a standardized and actionable format. For example, the EHR could generate a prompt for a follow-up call or a letter to be mailed if there is no evidence of a fill within a certain time period. Our results showed that the majority of patients who do fill their medications do so on the day it is prescribed, suggesting that interventions could be applied in the first few days following prescription. This approach has been used to improve the proportion of patients who fill statin prescriptions and could be broadened to other medication classes with appropriate supporting technology.7

Addressing nonadherence will undoubtedly require interventions that address a range of contributing factors including cost burden and access to medications; patient understanding, motivation, and behaviors; and the lack of coordinated care.20,21 However, aggregated claims data within the native EHR could serve as the foundation to more appropriately identify patients demonstrating nonadherence in real time in clinical practice.

Primary nonadherence is associated with adverse clinical outcomes, yet can be difficult to measure in a multi-payer environment. Our study used aggregated pharmacy fill data to identify that nearly one-third of patients prescribed a new antihypertensive medication in our primary care cohort did not fill that medication within 30 days. Our findings suggest that the increased availability of medication fill histories in clinical practice can provide objective insight into a patient’s medication adherence, and may provide a foundation for targeted interventions to improve primary nonadherence. 

 Author Affiliations: Thomas Jefferson University, Jefferson College of Population Health (DC, JC), Philadelphia, PA; Christiana Care Value Institute (RA, PW, DE), Newark, DE; Department of Medicine, Christiana Care Health System (DE), Newark DE.

Source of Funding: This project was funded by the Delaware Health Sciences Alliance pilot award, project order #7. Dr Comer was individually funded by the PhRMA Foundation Postdoctoral Fellowship for Health Outcomes.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DC, DE, JC, RA); acquisition of data (DE, RA); analysis and interpretation of data (DC, PW, DE, RA); drafting of the manuscript (DC, PW, DE); critical revision of the manuscript for important intellectual content (DC, DE, JC, RA); statistical analysis (PW, DE); provision of patients or study materials (DE); obtaining funding (DE); administrative, technical, or logistic support (DE, JC); and supervision (DE, JC).

Address correspondence to: Daniel Elliott, MD, MSCE, Associate Chair for Research, Department of Medicine, Christiana Care Health System, 4755 Ogletown-Stanton Rd, Newark, DE 19718. E-mail:

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