Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims

Abstract

Objective: Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications.

Materials and methods: We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states. We demonstrate an application of the method by calculating dispense rates for a set of commonly prescribed diabetes and cardio-protective medications. To further inform clinical care, we computed adjusted odds ratios of dispense by patient-, encounter-, and clinic-level characteristics.

Results: Seventy-six percent of cardio-protective medication prescriptions and 74% of diabetes medications were linked to a dispensing record. Age, income, ethnicity, insurance, assigned primary care provider, comorbidity, time on EHR, and clinic size were significantly associated with odds of dispensing.

Discussion: EHR prescriptions and pharmacy dispense data can be linked at the record level across different terminologies. Dispensing rates in this low-income population with diabetes were similar to other populations.

Conclusion: Record linkage resulted in the finding that CHC patients with diabetes largely had their chronic disease medications dispensed. Understanding factors associated with dispensing rates highlight barriers and opportunities for optimal disease management.

Trial registration: ClinicalTrials.gov NCT02685384.

Figures

Figure 1.

Process used to identify and match prescribed and dispensed medications. Notes: ADVANCE CDM=Common Data Model from the ADVANCE Clinical Data Research Network. RxCUI=RxNorm concept unique identifier. NDC=National Drug Code. RxMix=web interface from US National Library of Medicine, used to create mappings among different drug terminologies. Figure 1b. Representation of adjudication loop for matching medications. Description: Sort medication prescriptions and dispensing records by date within each patient. N=distinct patients with ≥1 prescription, M=maximum number of prescription records for any given patient, O=distinct patients with ≥1 dispensing record, and P=maximum number of dispensing records for any given patient.

Figure 1.

Process used to identify and match prescribed and dispensed medications. Notes: ADVANCE CDM=Common Data Model from the ADVANCE Clinical Data Research Network. RxCUI=RxNorm concept unique identifier. NDC=National Drug Code. RxMix=web interface from US National Library of Medicine, used to create mappings among different drug terminologies. Figure 1b. Representation of adjudication loop for matching medications. Description: Sort medication prescriptions and dispensing records by date within each patient. N=distinct patients with ≥1 prescription, M=maximum number of prescription records for any given patient, O=distinct patients with ≥1 dispensing record, and P=maximum number of dispensing records for any given patient.

Figure 2.

Odds of medication dispensed, by patient-, encounter-, and clinic-level factors. Footnote: Odds ratios and 95% CIs from GEE models with a logit link; robust standard error estimator applied to account for repeated measures within patients and patients nested within health centers. All models adjusted for sex, age group, and race/ethnicity. FPL predictor model restricted to facilities with sufficient variation for model conversion.