Pan Wu, PhD

Aggregate pharmacy claims available within an electronic health record (EHR) provide an opportunity to understand primary nonadherence in real time. The objective of this study was to use pharmacy claims data available within the EHR to identify the prevalence and predictors of primary nonadherence to antihypertensive drug therapy in a multi-payer primary care network.

We conducted a retrospective cohort study of patients prescribed a new antihypertensive medication in a large primary care practice network between January 2011 and September 2012.

We matched prescriptions for the new antihypertensive to pharmacy claims listed in the EHR. The primary outcome was the presence of a fill for the new medication within 30 days of the prescription.

Of 791 patients in our study cohort, two-thirds (522; 66%) filled their prescription within 30 days. The majority (409; 78.4%) of that group filled the prescription on the day it was issued. Lower diastolic blood pressure and Medicare coverage increased the probability of nonadherence.

Medication fill data within the provider EHR can identify primary nonadherence in clinical practice. As adoption of this technology increases, it provides an opportunity to identify nonadherence, allowing for the effective design of interventions to improve adherence to therapy.

	This study demonstrates the ability of aggregated pharmacy claims data available through the native electronic health record (EHR) to identify patients with primary nonadherence. The availability of multi-payer claims data within the EHR may serve as a foundation for ongoing medication monitoring and improving adherence in a nonintegrated primary care network.

Hypertension is the foremost modifiable risk factor for cardiovascular disease in US adults

; however, despite its importance, achieving blood pressure control remains challenging in routine clinical practice.

 Medication nonadherence is common and strongly associated with poor blood pressure control and adverse clinical outcomes.

 Traditionally, claims-based measures have focused on adherence after filling at least 1 prescription, or secondary adherence.

 More recently, links between prescribing information and claims records indicate that up to 30% of patients do not fill an initial antihypertensive prescription, which is referred to as primary nonadherence.

 Identifying patients who do not fill an initial prescription may be an important first step in addressing nonadherence and improving outcomes in clinical practice, particularly given recent evidence that outreach to patients with primary nonadherence can increase the proportion of patients who fill a prescribed medicine.

Primary nonadherence research has relied largely on pharmacy claims within integrated delivery systems or health plans.

 Historically, providers in most primary care practices, particularly those in multi-payer environments, do not have access to these data to identify or monitor for nonadherence. The recent adoption of electronic-prescribing (e-prescribing) systems has made prescription fill information increasingly available to providers within their native electronic health record (EHR). This access to aggregated, multi-payer pharmacy data creates an opportunity to identify and address primary nonadherence in clinical practice, possibly even in real time.

The objective of this study was to use aggregated pharmacy claims available within the practice-based EHR to identify the prevalence and predictors of primary nonadherence to antihypertensive therapy in primary care practice in a nonintegrated delivery system. Our findings will help to determine the potential value of aggregated pharmacy data in identifying primary nonadherence in routine clinical practice. This information could be the foundation for clinically meaningful and timely interventions to improve medication adherence.

We conducted a retrospective cohort study of patients prescribed a new antihypertensive medication in a primary care practice. We documented evidence of the first prescription fill from pharmacy claims available through the e-prescribing system within the provider EHR. The study was approved by the Institutional Review Board of Christiana Care Health System.

We conducted this study in a large, multi-specialty medical practice that includes 14 primary care sites providing care for more than 100,000 patients in northern Delaware and the surrounding communities. These practices share an EHR (Centricity, GE Healthcare), used for all clinical encounters, which contains a complete list of problems, prescriptions, allergies, office notes, and diagnostic test results. Additionally, all prescriptions in the practices are generated through the EHR, with e-prescribing in place since 2010. Through the e-prescribing platform, physicians or designees within the practice can request up-to-date pharmacy claim histories through Surescripts, the largest health information network in the United States. Accessing medication histories requires patient consent, which was incorporated into the patient registration process. The Surescripts medication history query provides aggregate pharmacy claims from the majority of retail and mail-order pharmacies, as well as from pharmacy benefit managers for private and public insurers. Once accessed, the medication history is available in the EHR.

We identified established patients with hypertension who were prescribed a new antihypertensive agent from January 2011 to September 2012. Patients included in the study needed to have a formal diagnosis of hypertension (

International Classification of Diseases, Ninth Revision, Clinical Modification

] code 401.xx) or elevated blood pressure (

 code 796.2) before the time of the index prescription. We required patients to have at least 1 primary care visit recorded in the EHR in the 18 months prior to the index visit date in order to avoid including patients whose prescriptions appeared to be new but actually represented a patient transitioning to a new provider or a practice transitioning from paper records to electronic records.

We excluded prescriptions if there was a previous prescription for a medication within the same class to avoid changes that were made in response to formulary requirements. To ensure sufficient observation time, we excluded patients whose pharmacy fill history was obtained less than 30 days after the index prescription, unless the pharmacy fill history showed evidence of filling the index prescription. We required patients to have evidence of at least 1 medication fill at any time in the entire pharmacy fill history, in order to minimize incorrectly labeling patients as nonadherent when the aggregated data source may not have included the patient’s pharmacy or payer information.

Our primary outcome was primary nonadherence, which we defined as having no evidence of filling the index prescription within 30 days. Previous studies have used 1 to 9 months as a cut point to classify primary nonadherence,

 but we specified 30 days as our time frame to maximize the clinical relevance of the information. The EHR provided patient demographics (age, gender, race, and insurance type) and clinical data (blood pressure, the total medication count, and the number of active antihypertensive medications at the time of the index visit). We measured comorbidity burden using the Elixhauser comorbidity index based on diagnoses related to previous encounters in our system.

 We measured previous healthcare utilization within our system as the count and time to prior-year hospitalizations, emergency department visits, and primary care provider visits. From a previous related study, we included a count of medication discrepancies between the medication lists recorded in the EHR and pharmacy claims at the time of the index prescription for a subgroup of our study cohort.

 In the previous study, we defined medication discrepancies as either a medication that was listed on the EHR prescriber list but did not have a corresponding fill or a filled medication that was not listed on the EHR prescriber list.

We used descriptive statistics to explain population characteristics associated with primary nonadherence at 30 days. We developed univariate and multivariate logistic regression models to examine the relationship of selected covariates with primary nonadherence. We used the same approach in the subset of patients for whom the number of medication discrepancies at the time of the index visit was available to determine sensitivity of results to potential EHR errors. All analyses were done with SAS version 9.3 (SAS Institute, Cary, North Carolina).

 shows the development of our cohort. Of 15,781 patients who received a new prescription for an antihypertensive medication, 791 patients met our study definition. The majority of patients (11,207; 71%) were excluded because they did not have available pharmacy fill records to address our study question. Of those excluded, 8721 had no imported pharmacy fill history, 1998 had no pharmacy claims recorded, and 488 had pharmacy records that had fewer than 30 days of observation following the index visit.

 describes our cohort. Two-thirds of the study population (522/791; 66%) filled prescriptions within the first 30 days. The majority of patients (409/522; 78.4%) who filled their prescriptions within 30 days filled it on the same day the prescription was issued.

 shows the results of regression analyses for predicting first-fill nonadherence. In the univariate analysis, patients without evidence of a fill were more likely to be older (odds ratio [OR], 1.01; 95% CI, 1.00-1.02), have Medicare (OR, 1.61; 95% CI, 1.16-2.23), and have a higher number of medications (OR, 1.04; 95% CI, 1.00-1.07). Having a higher diastolic blood pressure at the index visit was associated with a lower rate of nonadherence (OR, 0.98; 95% CI, 0.97-1.00). In the multivariate model, increasing diastolic blood pressure was found to be significantly associated with decreased nonadherence (OR, 0.98; 95% CI, 0.97-1.00) and having Medicare was found to be significantly associated with increased nonadherence (OR, 1.57; 95% CI, 1.02-2.42). The results of our subgroup analysis contained 502 patients for whom the number of medication discrepancies was available. Patients with more medication discrepancies at baseline were more likely to meet our definition of nonadherence (OR, 1.30; 95% CI, 1.15-1.46).

Primary nonadherence is an increasingly identified barrier to optimal hypertension management. However, the ability to intervene has been limited by the lack of pharmacy claims records available in clinical practice in nonintegrated delivery systems. We used aggregated pharmacy claims data available through the primary care EHR to determine that one-third of patients prescribed a new antihypertensive medication in our cohort lacked evidence of filling the medication within 30 days. This is consistent with Fischer et al who identified a 28.4% incidence of primary nonadherence in a similar cohort.

 Lower rates of primary nonadherence have been found in studies within integrated delivery systems,

 perhaps reflecting the ability of an integrated delivery system to capture more complete medication refill history. Although the Surescripts network aggregates the majority of payers in the United States, it may not have complete capture in our multi-payer environment. Differences in settings and methods of data capture may explain these differences in primary nonadherence rates.

We identified the association of primary nonadherence with increasing medication burden, age, and noncardiovascular comorbidities. This is consistent with previous literature suggesting that medication nonadherence may increase as competing comorbidities, particularly with active symptoms, take precedence over asymptomatic conditions such as hypertension.

 Although the evidence for association of primary nonadherence with comorbidity is mixed, with some evidence suggesting increased primary nonadherence with a higher medication burden,

 our study and others suggest the opposite.

Interestingly, we identified that patients with lower diastolic blood pressure were more often nonadherent. The association may indicate a perceived lack of urgency, particularly given the asymptomatic nature of hypertension, although this is not clear from our data. Specific interventions for those with lower grades of hypertension may need to include education on the importance of adherence to prevent worsening hypertension.

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.

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.

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.

 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. 

 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.

 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.

 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.

 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).

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

1. Lloyd-Jones D, Adams RJ, Brown TM, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2010 update: a report from the American Heart Association. 

2. Vital signs: awareness and treatment of uncontrolled hypertension among adults—United States, 2003—2010. CDC website. http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6135a3.htm. Published September 7, 2012. Accessed April 18, 2014.

3. Schroeder K, Fahey T, Ebrahim S. How can we improve adherence to blood pressure-lowering medication in ambulatory care? systematic review of randomized controlled trials. 

Articles

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. 


Using Aggregated Pharmacy Claims to Identify Primary Nonadherence

 Methods for efficient medication reconciliation are increasingly important in primary care. Aggregated pharmacy data within the native electronic health record (EHR) may create a new opportunity for efficient and systematic medication reconciliation in practice. Our objective was to identify the prevalence and predictors of medication discrepancies between pharmacy claims data and the medication list in a primary care EHR.

 We conducted a retrospective cohort study of patients prescribed a new antihypertensive in a large primary care practice network between January 2011 and September 2012. We compared patients’ active medications recorded in the practice EHR with those listed in pharmacy claims data available through the EHR. The primary outcome was the presence of a medication discrepancy.

 Of 609 patients, 468 (76.9%) had at least 1 medication discrepancy. Significant predictors of discrepancies included the total medication count (odds ratio [OR], 2.18; 95% CI, 1.85-2.57) and having a recent emergency department visit (OR, 2.58; 95% CI, 1.03-6.45). The identified discrepancies included 171 patients (28.1%) with 229 controlled substance discrepancies.

 Our study revealed a high rate of discrepancies between pharmacy claims data and the provider medication list. Aggregated pharmacy claims data available through the EHR may be an important tool to facilitate medication reconciliation in primary care.

		Aggregated pharmacy claims data are increasingly available within a provider electronic health record.

		Our study suggests that this data may provide a foundation for assessing medication adherence and conducting medication reconciliation.

		Optimizing the accessibility and function of this data should be a high priority as the primary care information technology infrastructure expands.

Medication reconciliation is “the process of comparing a patient’s medication orders to all of the medications that the patient has been taking.”

Strong evidence supports the value of reconciliation in inpatient settings

 leading to The Joint Commission requirement for medication reconciliation at hospital admission and discharge.

However, the benefit of medication reconciliation may have the most impact in ambulatory settings, where discrepancies frequently occur between physician medication orders in the electronic health record (EHR) and what the patient is actually taking.

Given that 3 of 4 physician office visits yield at least 1 new prescription,

such discrepancies likely contribute to the estimated 3.3 million serious preventable outpatient medication errors

 and 1.9 million adverse drug event-related visits annually in the United States.

As a result, national programs including Meaningful Use and the National Committee for Quality Assurance Medical Home Certification now require more frequent and systematic medication reconciliation in primary care practice.

Despite its recognized importance, medication reconciliation is challenging.

Physicians cite lack of time as a barrier, and most practices do not have access to resources such as clinical pharmacists to support reconciliation.

 Improvements in health information technology may facilitate accurate medication reconciliation in real time,

and federal incentives have increased the adoption of electronic prescribing (e-prescribing).

These platforms often make aggregated pharmacy claims available to providers, frequently within the native EHR. Claims data are a proven source for medication reconciliation,

 but few practices outside of large managed care organizations have access to these data.

The purpose of this study was to evaluate aggregated pharmacy claims available through the EHR of a large primary care network as a source for estimating the prevalence and identifying the predictors of medication discrepancies between claims data and the medication list in the primary care EHR. Our secondary aim was to determine the factors associated with discrepancies involving high-risk medications, including controlled substances. Our findings will provide a first step toward identifying the potential benefit of using aggregated claims data as a platform for improving the efficiency and quality of medication reconciliation in primary care.

We conducted a retrospective cohort study of established patients who were prescribed a new antihypertensive during a primary care office visit. We compared medications listed in the practice EHR with those identified from pharmacy claims data available through the EHR. We identified and characterized discrepancies between the 2 lists, simulating medication reconciliation at the time of the new prescription. The study was approved by the Christiana Care Institutional Review Board.

We conducted this study in a large multi-specialty medical practice, which includes 14 primary care sites providing care for over 100,000 people in northern Delaware and the surrounding communities. These practices share an EHR (Centricity from GE Healthcare) used for all clinical encounters. All prescriptions in the multi-specialty practices are generated through the EHR, and e-prescribing has been available since 2010.

Providers can request aggregated pharmacy claims data in real time within the EHR. Patients must provide consent, which was incorporated into the patient registration process. Surescripts provides the pharmacy data, including claims from retail and mail-order pharmacies, and from pharmacy benefit managers for private and public insurers.

We identified established patients within the practices who were prescribed a new antihypertensive medication at an office visit between January 2011 and September 2012. We required a diagnosis of hypertension (

 code 796.2) in the EHR at the time of prescription. We required patients to have had at least 1 primary care visit recorded in the EHR in the 18 months prior to the index visit in order to eliminate patients who were new to the practice or whose practices were transitioning from paper records to electronic. To avoid medication changes as a result of formulary requirements, we only included prescriptions for a new drug class. Only the first qualifying prescription per patient was considered.

We included patients with a pharmacy claim history imported to the EHR on or after the index visit in order to capture all claims prior to the index visit. We required evidence of at least 1 claim in the pharmacy claim history to minimize mislabeling medications as missing for patients whose claim history was not available in the data source.

To identify medication discrepancies, we generated separate lists of active medications at the time of the index visit for the provider’s EHR and the pharmacy claims. We excluded medications for which the days’ supply could not be calculated (ie, over-the-counter, insulin, and ophthalmics). We collapsed medications by generic name regardless of dose or frequency. Active medications from the provider’s EHR were those with a start date in the EHR prior to the index date, and the stop date, if present, was on or after the index visit. We did not require evidence of a prior prescription from the EHR, as providers routinely document medications prescribed by other providers.

From the pharmacy claims history, we identified all medication claims within the 120 days prior to the index visit. We calculated the end date for the most recent fill for each medication as the days’ supply plus a 15-day grace period to account for adherence variability and oversupply. We considered medications active if the calculated end date fell on or after the index visit. One author (DC) classified all medications. When validating a random 10% sample, a second pharmacist identified 6 errors among 977 medications—an error rate of 0.61%&mdash;which we considered acceptable.

After finalizing each medication list, we manually compared them to simulate medication reconciliation. Our primary outcome was the presence of a medication discrepancy, defined as a medication present in only 1 source. This interpretation aligns with previous definitions of unintentional medication discrepancies,

which have been associated with higher rates of potential adverse drug events.

Demographic variables from the EHR at the index visit included patient age, gender, race, and primary insurance. We included clinical variables such as the total number of medications from both the EHR and claims data. We also included the number of active antihypertensive medications and blood pressure at the index visit. We measured patient comorbidity using the Elixhauser index, and classified these as either cardiovascular (CV)-related or unrelated comorbidities.

 We included previous healthcare utilization, including counts and timing of hospitalizations, emergency department (ED) visits, and primary care visits at our institution.

We used χ2 and t tests to determine the association of discrepancies with covariates. We then developed multivariate logistic and log-linear regression models. Variables included in the final models include those statistically significant in the univariate model and those deemed clinically relevant. As total medication count at index visit was a strong predictor, we generated versions of both models with and without this variable. The results of the logistic and log-linear regression models were very similar; we present only the results of the logistic regression models. We separately developed similar models for controlled substance discrepancies. We conducted sensitivity analyses excluding antibiotics and antifungals, but these did not change our models significantly so they are not reported. All analyses were done with SAS version 9.3 (SAS Institute, Cary, North Carolina).

shows the development of our cohort. Of 15,781 patients who had a new prescription generated for an antihypertensive medication during the study period, 3284 met our initial patient criteria. Of these, 2675 did not have a medication refill history that met criteria. Our final study population included 609 patients; the patients included in our final cohort had a larger proportion of blacks (

= .05), but were similar to the previous 3284 in regard to other demographic characteristics. The 609 patients in our study cohort accounted for 2947 medications meeting study definition. Of these, 1401 (47.5%) were identified as discrepancies, 831 (59.3%) appeared only in the EHR, and 570 (40.7%) only in the pharmacy claims.

The majority of patients (468 of 609; 76.8%) had at least 1 medication discrepancy (mean ± SD = 2.3 + 2.4). Characteristics of patients with and without a discrepancy appear in 

. Patients with a discrepancy were more likely to have a hospitalization in the past year (23.9% vs 10.6%; 

= .001), noncardiovascular comorbidities (1.4% vs 1.1%; 

<.001), and a higher total medication count (5.6 ± 3.5 vs 2.4 ± 1.7; 

<.001). Among patients with discrepancies, 116 of 468 (24.8%) had medications in the provider list but not in the fill history, 186 (39.7%) in the fill history but not the provider list, and 166 (35.5%) had discrepancies from both the provider list and fill history.

 shows the multivariate logistic regression model for having at least 1 medication discrepancy; total medication count was strongly associated with the presence of a discrepancy (odds ratio [OR], 2.18; 95% CI, 1.85-2.57). Other significant covariates included being female (OR, 1.54; 95% CI, 1.05-2.26) and count of noncardiovascular comorbidities (OR, 1.34; 95% CI, 1.11-1.54). When we excluded total medication count from the model, being female, having a recent ED visit, or experiencing a hospitalization in the past year were all associated with increased odds of discrepancy.

We identified 229 discrepancies involving a controlled substance among 171 patients (28.1%); 139 (60.1%) were in the EHR without a corresponding fill and 90 (39.9%) were filled without appearing in the EHR. 

 shows the multivariate analysis predicting 1 or more controlled substance discrepancies. Total medication count (OR, 1.27; 95% CI, 1.19-1.36) and female gender (OR, 1.66; 95% CI, 1.10-2.49) were significant covariates associated with increased risk. When we excluded total medication count from the model, an ED visit in the past 90 days was significantly associated with a controlled substance discrepancy.

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%.

 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.

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,

are more likely to have discrepancies. Patients with multiple medications and comorbidities frequently see many providers,

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.

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 completeness

 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.

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,

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.

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,

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.

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.

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

 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.

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.

 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).

 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: 

The authors used aggregate pharmacy claims data available within a primary care electronic health record to identify a high rate of medication discrepancies.

Pan Wu, PhD

Articles

Using Aggregated Pharmacy Claims to Identify Primary Nonadherence

Usefulness of Pharmacy Claims for Medication Reconciliation in Primary Care