The impact of the 4R Oncology model on OncotypeDx turnaround time.
Authors: Wei Jenny , Weldon Christine B. , Trosman Julia R. , Zhu Zheng , Linehan Elizabeth Shurell , Lin Amy Ying Ju , Abbe Thea , Sakoda Lori C. , Gordon Nancy P. , Ravelo Arliene , Shiraz Aida , Liu Raymond 
Published: June 2023

531 Background: The OncotypeDx genomic assay has been widely used to predict recurrence in those with early-stage ER positive breast cancer to determine whether adjuvant chemotherapy is beneficial. Receiving OncotypeDx results in a timely manner prior to medical oncology appointment is critical to inform adjuvant therapy decisions. In this quality improvement project, we implemented a care delivery optimization for patients with breast cancer receiving OncotypeDx assays in an integrated healthcare system. The optimization was conducted as part of implementing the 4R Oncology model (Right Info / Right Care / Right Patient / Right Time), a novel care model that aims to optimize patient-centric care planning, team-based delivery and patient self-management. Herein, we assess the impact of implementing this model on improving OncotypeDx turnaround time. Methods: We examined two patient groups with newly diagnosed early-stage ER positive breast cancer who attended a multidisciplinary clinic and underwent surgery at a single medical center. The historical control (HC) group received care pre-4R implementation, from January 2019 through June 2020, while the 4R Intervention group received care from July 2020 to December 2021. The OncotypeDx-related care optimization was a team effort of 5 medical specialties that involved establishing an OncotypeDx reflex testing practice, updating workflows, working with a testing company to streamline the process and implementing a method to align result timing with scheduling medical oncology appointments. Bivariate analyses were used to compare the HC and 4R groups on demographic characteristics and OncotypeDx turnaround time. Results: Within the 4R group (N = 208), 72 (34.6%) patients received OncotypeDx and 89 (32.5%) patients from the HC cohort (N = 274) received OncotypeDx. The groups were balanced by median age at time of multidisciplinary clinic visit (HC: 59 years vs 4R: 60 years), female gender (HC: 99.6% vs 4R: 99.5%), race, combined race and ethnicity, language spoken, Charlson Comorbidity Index and tumor characteristics. The average turnaround time from surgery date to delivery of OncotypeDx results was significantly reduced in the 4R cohort (19.4 days) compared to that of the HC cohort (23.1 days), a difference of 3.8 days, p = 0.004. The availability of OncotypeDx results by the first medical oncology visit after surgery increased from 40% in the HC cohort to 69% in the 4R cohort, p = 0.0003, helping optimize the transition to adjuvant therapy for the appropriate population. Conclusions: The implementation of the 4R Oncology model within an integrated healthcare system significantly reduced turnaround time for patients with breast cancer awaiting OncotypeDx results, improving patient-centric care and enabling informed discussions at medical oncology visits. The 4R Oncology model is an effective method to optimize delivery of time-sensitive care.