JMIR Publications

ABSTRACT

Background:

Comprehensive models of survivorship care are necessary to improve access and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment.

Objective:

This paper presents our expert-informed, rules-based, survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa) 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 and refined though 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.