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Purpose: This retrospective cohort study evaluated whether linked electronic health record (EHR) pharmacy data were adequately complete and timely to detect primary nonadherence to breast cancer adjuvant endocrine therapy (AET)., Materials and Methods: Linked EHR pharmacy data were extracted from the EHR for patients with stage 0 to III breast cancer who had their first prescription order for AET between 2016 and 2021. Patients with the first dispense event within 90 days of the prescription were classified as having sufficient or insufficient data available for early detection of primary adherence., Results: A total of 1,446 eligible patients had a first AET prescription order between 2016 and 2021; these orders were routed to 871 unique pharmacies, of which 856 (98.2%) were contracted with the linked EHR pharmacy database and 15 (1.8%) were not contracted. Among the 1,428 patients with a first prescription sent to a contract pharmacy, 164 (13%) had incomplete linked EHR pharmacy data refresh events to assess primary adherence. Among the 1,244 patients with at least 1 refresh event after their first prescription, 82% occurred within 90 days and were sufficiently timely for early detection of primary adherence. Overall, 32% of patients would benefit from an intervention to verify or improve primary adherence to AET., Conclusion: Although linked EHR pharmacy data have adequate completeness of contract pharmacy data, local configurations of data refresh events tailored to medication reconciliation workflows are incomplete (13%) and insufficiently timely (32%) to fully support clinical decision support (CDS) for early detection of primary medication nonadherence. Prospective CDS interventions using linked EHR pharmacy data are possible with enhancements to the frequency and timeliness of refresh events.

Purpose: Tumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching., Methods: Clinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated., Results: Manual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches., Conclusion: Reflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.