Reducing effort through informatics-driven identification, recruitment, and data capture on a trial to improve adjuvant endocrine therapy adherence.
Abstract
11025 Background: Adjuvant endocrine therapy (AET) improves outcomes in HR+ breast cancer. Nonadherence to AET is common, but manual screening for nonadherence is fraught. High-specificity structured cancer electronic health record (EHR) data, particularly if it includes key variables like stage, could streamline these steps for both clinical care and research purposes. We conducted an analysis of approaches to identifying patients as eligible for a multi-site AET adherence study to understand how variation in structured data informatics-supported screening impacts coordinator time. Methods: Mayo Clinic implemented a two-step identification: (1) EHR structured data (diagnosis date, stage, ER/PR/HER2 positivity, medication script) to prescreen for eligibility; (2) SureScripts fills to flag nonadherence using proportion of days covered < 80% across early fills. Medical College of Wisconsin (MCW) implemented a more manual screening process for Step 1 using only medication scripts. Coordinators at both sites then conducted a medical record review to assess if the resulting list of patients had a plan to continue AET for at least two additional years. Opt-out clinician permission preceded patient contact; pharmacists delivered a patient reported outcome (PRO)-guided intervention and documented encounters. Results: At Mayo, 829 patients were prescreened for study eligibility through informatics workflow; 120 (14.5%) met eligibility criteria. At MCW, 1,763 patients were prescreened; 168 (9.5%) were eligible. EHR filters included non–breast cancer encounters (50% at MCW) and ineligible based on time since cancer (36% at Mayo; 26% at MCW). At Mayo, screening required 5.6 minutes/patient for experienced coordinators. At MCW, experienced coordinators averaged 6.67 minutes/patient and inexperienced coordinators averaged 9.33 minutes/patient. Conclusions: Structured EHR data, pharmacy-fill information, and standardized workflows allowed consistent prescreening, intervention and recruitment processes across sites. Higher-specificity informatics-supported screening reduced the percentage of patients requiring manual review and reduced coordinator screening time. It also supported timely provider-permission steps and patient outreach. These workflows were feasible to implement at both institutions, and study enrollment is ongoing. Clinical trial information: NCT06765707 .
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (11)
Ahauve Orusa
Mayo Clinic Rochester, Rochester, MN
Joan Marie Neuner
Medical College of Wisconsin, Milwaukee, WI
Kathryn Jean Ruddy
Department of Oncology, Mayo Clinic Rochester, Rochester, MN
Becca Benoy
Medical College of Wisconsin, Milwaukee, WI
David Huschka
Mayo Clinic Rochester, Rochester, MN
Rama Volety
Mayo Clinic Rochester, Rochester, MN
Nicole Larson
Mayo Clinic Rochester, Rochester, MN
Victoria Dolmann
Mayo Clinic Rochester, Rochester, MN
Jodi L. Taraba
Mayo Clinic Rochester, Rochester, MN
Kathryn E. Flynn
Department of Medicine, Medical College of Wisconsin, Milwaukee, WI
Amye Juliet Tevaarwerk
Mayo Clinic Rochester, Rochester, MN