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Usefulness of Pharmacy Claims for Medication Reconciliation in Primary Care
Dominique Comer, PharmD, MS; Joseph Couto, PharmD, MBA; Ruth Aguiar, BA; Pan Wu, PhD; and Daniel J. Elliott, MD, MSCE
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Usefulness of Pharmacy Claims for Medication Reconciliation in Primary Care

Dominique Comer, PharmD, MS; Joseph Couto, PharmD, MBA; Ruth Aguiar, BA; Pan Wu, PhD; and Daniel J. Elliott, MD, MSCE
The authors used aggregate pharmacy claims data available within a primary care electronic health record to identify a high rate of medication discrepancies.
Recent policy changes including Meaningful Use and the Medical Home Certification process increasingly emphasize medication reconciliation in primary care, prioritizing methods for efficient and systematic medication reconciliation in routine practice. We used aggregated pharmacy fill data available through a provider EHR to simulate reconciliation between the provider medication list and pharmacy fill history in a cohort of patients prescribed a new antihypertensive medication. In our cohort, more than 75% of patients had at least 1 discrepancy, involving nearly half of all medications. Patients with higher medication counts and higher previous healthcare utilization were at increased risk of discrepancies.

Our findings are consistent with previous literature, which, using various methodologies, has identified discrepancy rates in the outpatient setting from 26% to 97%.6,7,27,28 Importantly, we identified only discrepancies based on drug name and not on differences in dose and frequency; had we considered these additional factors, the number of discrepancies likely would be greater. While our high discrepancy rate may reflect incomplete data in our source, 40% of the discrepancies in our sample involved medications appearing in the claims history but not recorded in the EHR, suggesting that even if incomplete, pharmacy fill data provide valuable information.

This high rate of discrepancies is alarming, particularly given the association of discrepancies with adverse events.29 Consistent with prior research, discrepancies in our cohort occurred in patients with markers of increased comorbidity and utilization. Patients with a higher total medication count, a recognized surrogate for medical complexity,30 are more likely to have discrepancies. Patients with multiple medications and comorbidities frequently see many providers,31 making reconciliation within primary care particularly challenging. We anticipated that frequent primary care office visits would provide opportunity to conduct medication reconciliation, minimizing discrepancies, but did not see this association.

Importantly, we identified that nearly a third of our patients had a discrepancy involving controlled substances. This is an area of increasing emphasis owing to concerns about diversion or misuse. Taken together, our findings provide strong evidence that claims data available through the EHR contain meaningful information while further underscoring the importance of medication reconciliation among patients with high comorbidity burden, particularly following ED or hospital visits, as emphasized in Stage 2 of Meaningful Use.13

Limitations

Before considering the implications of our findings, it is important to recognize several limitations. First, we used aggregated pharmacy data that may be incomplete, and our findings may overestimate the proportion of medications that are prescribed in the EHR but not filled. Second, slightly fewer than 20% of patients in our applicable patient population met study criteria, often resulting from unavailable pharmacy records, suggesting that providers were not routinely accessing these data in practice. Patients in our final cohort had a greater proportion of black and female patients, perhaps indicating selection bias in the patients or practices in which this data is accessed in clinical practice. Finally, physicians may be more likely to conduct medication reconciliation at the time of a new prescription, so the medication lists we are comparing may have already been adjusted by physicians. In that case, our findings could represent a “best case scenario.”

Despite these limitations, our findings provide insight into the potential for improved access to pharmacy claims data through an expanding health information technology infrastructure to facilitate medication reconciliation. Previous research suggests that claims data can improve the completeness20 and accuracy21 of medication reconciliation, and we have demonstrated that aggregated pharmacy fill data can be used to identify medication discrepancies at the individual patient level in a multi-payer primary care network.

Implications

Our study has important implications for the use of aggregated pharmacy fill data. First, this information could be used to help providers identify medications prescribed by other providers, potentially minimizing drug interactions or duplication. This is particularly important for narcotic prescriptions, where tracking and monitoring for diversion and misuse are well-recognized goals for providers. Secondly, this information could help providers identify medication nonadherence. Previous literature suggests that providers have limited ability to reliably assess adherence in clinical practice,32 and claims data can provide objective information to inform decision making. Finally, given the significant demands on primary care practices, our results suggest that these data may have the most potential for benefit in patients at higher risk for adverse events, such as those with higher rates of comorbidity or healthcare utilization.

There are barriers to overcome if these data are to impact clinical care. First, the aggregated data must be complete. In terms of narcotics, many states have drug monitoring programs for complete tracking of narcotic fills, regardless of source, and the exchange of these data within the EHR is being considered for Stage 3 of Meaningful Use.33 Joining these data with aggregated pharmacy data would have the potential to provide more complete data to providers. Secondly, the EHR platform should provide an efficient mechanism to identify and prompt clinicians about discrepancies in real time. Despite available data, few providers in our system accessed pharmacy claims through the EHR, suggesting that future efforts to use this information meaningfully will require making it readily available and actionable. Finding methods to provide the data objectively, such as automated assessments of discrepancy counts and validated adherence estimates,34 will be necessary for optimal incorporation of this information in practice. Third, the EHR should provide an easy mechanism to incorporate identified medications automatically into the medication list, allowing providers to take advantage of the EHR functionality to identify interactions.

CONCLUSIONS

Medication reconciliation is increasingly important but challenging to conduct in primary care. Our findings suggest that aggregated pharmacy claims data available within the provider EHR can be used to identify discrepancies at the individual level in a multi-payer setting. Availability of this information in real time should be made a priority for health information technology efforts, as aggregated pharmacy claims may provide an optimal foundation for efficient and high-quality medication reconciliation.

Acknowledgments

The authors would like to acknowledge Suraj Rajasimhan, PharmD, for his contribution to this study.

Author Affiliations: Jefferson School of Population Health (DC, JC), Philadelphia, PA; Christiana Care Value Institute (RA, PW, DJE), Newark, DE; Department of Medicine, Christiana Care Health System (DJE), 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, JC, DJE, RA); acquisition of data (DJE, RA); analysis and interpretation of data (DC, DJE, RA, PW); drafting of the manuscript (DC, DJE, PW); critical revision of the manuscript for important intellectual content (DC, JC, DJE, RA); statistical analysis (DJE); provision of study materials or patients (DJE); obtaining funding (DJE); administrative, technical, or logistic support (JC, DJE); and supervision (JC, DJE).

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