| Popis: |
BACKGROUND Comprehensive models of survivorship care are necessary to improve access and coordination of care and to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment. Our group is building a nurse-led virtual clinic to support men living with prostate cancer (PCa) in the post-treatment follow-up phase of their survivorship journey. OBJECTIVE This paper presents our expert-informed, rules-based, survivorship algorithm to build a nurse-led model of survivorship care for prostate cancer survivors with “no evidence of disease” (Ned) to support more timely decision-making, enhanced safety and continuity of care. METHODS An initial rule-set was developed via a literature review and working groups with clinical experts across Canada (e.g., nurse experts, physician experts, scientists) (n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using nominal group technique. RESULTS Four levels of alert classification were established, initiated by responses on the EPIC-CP survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation. CONCLUSIONS The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse to patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support more timely decision-making, enhance continuity of care through automation of more frequent automated check points, while empowering patients to self-manage their symptoms more effectively than standard care. INTERNATIONAL REGISTERED REPORT RR2-10.1136/bmjopen-2020-045806 |
