Time to Rethink the Role of Clinical Pathways in the Era of Precision Medicine: A Lung Cancer Case Study

Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness

OBJECTIVES

The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing.

DATA SOURCES

Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use.

CONCLUSION

The combination of diverse data sets and data science analytic approaches has the potential to extend precision oncology nursing research and practice. Integration of this framework can be implemented within a learning health system where models can update as new data become available across the continuum of the cancer care trajectory. To date, data science approaches have been underused in extending personalized toxicity assessments, precision supportive care, and enhancing end-of-life care practices.

IMPLICATIONS FOR NURSING PRACTICE

Nurses and nurse scientists have a unique role in the convergence of data science applications to support precision oncology across the trajectory of illness. Nurses also have specific expertise in supportive care needs that have been dramatically underrepresented in existing data science approaches thus far. They also have a role in centering the patient and family perspectives and needs as these frameworks and analytic capabilities evolve.

Impact of clinical pathways on treatment patterns and outcomes for patients with non-small-cell lung cancer: real-world evidence from a community oncology practice

Introduction: The evolving treatment landscape for non-small-cell lung cancer (NSCLC) and complexities of regulations and reimbursement present challenges to community oncologists. Clinical pathways are tools to optimize care, but information on their value in the real world is limited. This retrospective study assessed treatment patterns and clinical outcomes in patients with Stage I-III NSCLC pre- and post-pathways implementation at Tennessee Oncology, a large, community-based oncology practice in the USA. Methods & Materials: Chart data were abstracted for adults diagnosed with Stage I-III NSCLC who received systemic treatment. Patients were divided into pre-pathways (treatment initiation 2014-2015) and post-pathways (treatment initiation 2016-2018) cohorts. Patient characteristics, treatment patterns and outcomes were summarized descriptively. Kaplan-Meier curves were used to assess time-dependent outcomes, and log-rank test was used to compare the cohorts. Results: 291 patients were included (Stage I-II: 38 pre-pathways, 55 post-pathways; Stage III: 105 pre-pathways, 93 post-pathways). Duration on first-line (1L) therapy was similar for Stage I-II patients pre- and post-pathways (median 1.9 months vs 2.1 months; p = 0.75), but increased for Stage III patients post-pathways (2.1 months vs 1.4 months pre-pathways; p < 0.01). Achievement of a complete or partial response with 1L therapy was similar post-pathways among Stage I-Stage -IIII patients (60.0% vs 55.2% pre-pathways), but increased for Stage III patients (56.0% vs 35.2% pre-pathways). Conclusion: Given that improvements in rates of treatment response post-pathways occurred only for patients diagnosed with Stage III NSCLC, among whom immunotherapy uptake increased post-pathways, such improvements may be attributable to evolving practices in cancer care, including advances in treatment and care delivery, rather than clinical pathways implementation. Further research is warranted to assess the impact of clinical pathways in the current treatment era, given that immunotherapy has now become the standard of care in NSCLC.