Optimizing Cancer Care Delivery Through High-Functioning Teams: Results of Implementing the 4R Oncology Model in Lung...

Cancer care is highly interdependent, but optimal timing and sequencing is challenging. Teamwork is a promising approach to optimize care timing and sequencing. We previously showed that the 4R Oncology model fostered a high-functioning team and enabled interdependent care optimizations in lung and breast cancers in a community health system. Herein, we evaluated whether the optimizations and 4R clinic implementation resulted in actual timing/sequencing improvements.

We activated optimizations and implemented Care Sequences in practice. Care Sequences are 4R tools for patients and clinicians who guide optimal roadmap and timing/sequencing of care. In each cancer, we compared metrics for 11 types of interdependent care using chi-square analyses between the intervention cohort (lung cancer n = 138, breast cancer n = 208), diagnosed post-4R, and the historical cohort (lung cancer n = 173, breast cancer n = 268), diagnosed pre-4R. We used multiple regression analysis to determine factors influencing a composite Optimization Index, a patient-level measure of overall interdependent care optimization.

Intervention and historical cohorts were comparable in patient characteristics and care received. In each cancer, timing/sequencing for all care types improved, six of them significantly, including the timing of lung surgery (88% v 72%, P = .02), lung biomarker results (81% v 63%, P = .04), breast gene expression results (70% v 34%, P < .001), and endocrine therapy start (89% v 78%, P = .03). Optimization Index was significantly higher in the intervention than historical cohorts (P < .001 in each cancer, mean = 0.82 v 0.68 in lung cancer; 0.81 v 0.68 in breast cancer). 4R contributed to this increase twice as much as all patient characteristics and care received combined.

4R is effective in improving timing and sequencing of interdependent care via establishing high-functioning teams and facilitating relevant optimizations. Our study provides a roadmap for other institutions developing high-functioning teams and optimizing timeliness of care.