Innovating the Personalization of Stratified Survivorship Care Pathways: Using a Cancer Data Ecosystem to Improve Care Access, Outcomes, Efficiency, and Costs.

New models of survivorship care are needed that improve outcomes for the growing number of cancer survivors, address the increasing complexity of their health needs, and deal with the shortage of clinicians and rising costs of this care. Technology can aid the delivery of personalized, stratified survivorship care pathways where the intensity of care, the care setting, and the providers required for that care vary with survivors' needs. Building a cancer data ecosystem of connected data streams that supports and learns from each patient can be used to streamline care, enhance efficiency, reduce costs, and facilitate research. This manuscript describes the input, analytics, and output components of the cancer data ecosystem that must be built and connected and also provides a real-world use case of how such a system could transform care in a large US comprehensive cancer center.

Identifying Optimal Program Models and Clinical Tools for Follow-Up Care.

Fifteen years ago, the Institute of Medicine (IOM) issued a report that defined Survivorship Care as a distinct phase of the cancer care continuum. The required domains to meet the health needs of cancer survivors were outlined in the report: cancer surveillance and screening, cancer prevention and lifestyle counseling, management of treatment related persistent or late effects, coordination of care, and psychosocial support services. In response to that report, The University of Texas MD Anderson Cancer Center implemented a tiered survivorship care model that is risk based. The core principle is that cancer survivors' health needs will depend on the cancer treatment and disease-specific risks. We here describe this model for low-, intermediate-, and high-risk cancer survivors, in which comanagement between oncology and primary care providers is risk dependent. Our clinical model defines transition as appropriate when there is a minimal risk of primary cancer relapse, which is specific to each cancer type and disease stage. This model is embedded into disease-specific clinical practice algorithms, aligned with the IOM domains of care. Over the past 10 years, we have successfully transitioned nearly 25 000 patients to disease-specific survivorship clinics, providing care based on the IOM domains. We have learned from our process that expansion of survivorship care into established clinical settings requires engagement of champions and key clinical stakeholders. Future directions for survivorship care should explore the application and potential benefits of telemedicine as a care delivery system to meet the needs of cancer survivors.

Assessing patient-reported symptom burden of long-term head and neck cancer survivors at annual surveillance in survivorship clinic.

BACKGROUND: This study reports long-term head and neck cancer (HNC) patient-reported symptoms using the MD Anderson Symptom Inventory Head and Neck Cancer Module (MDASI-HN) in a large cohort of HNC survivors. METHODS: MDASI-HN results were prospectively collected from an institutional survivorship database. Associations with clinicopathologic data were analyzed using χ2 , Mann-Whitney, and univariate regression. RESULTS: Nine hundred and twenty-eight patients were included. Forty-six percent had oropharyngeal primary tumors. Eighty-two percent had squamous cell carcinoma. Fifty-six percent of patients had ablative surgery and 81% had radiation therapy as a component of treatment. The most severe symptoms were xerostomia and dysphagia. Symptom scores were worst for hypopharynx and varied by subsite. Patients treated with chemoradiation or surgery followed by radiation ± chemotherapy reported the worst symptoms while patient treated with surgery plus radiation ± chemotherapy reported the worst interference. CONCLUSION: HNC survivors describe their long-term symptom burden and inform efforts to improve care many years into survivorship.