An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study

Area of Science:

• Oncology
• Nursing
• Health Services Research

Background:

• Improved survivorship care models are crucial for managing long-term physical and psychosocial needs of cancer survivors.
• Prostate cancer (PCa) survivorship presents unique challenges requiring specialized care coordination.

Purpose of the Study:

• To present the expert-informed, rules-based No Evidence of Disease (Ned) algorithm for a nurse-led PCa survivorship care model.
• To enhance decision-making, safety, and continuity of care for men with prostate cancer.

Main Methods:

• Developed an initial rule set refined by clinical experts and patient partners in Canada.
• Defined algorithm priorities through multidisciplinary consensus meetings.
• Validated and refined the system using the nominal group technique.

Main Results:

• Established four alert classification levels based on survey responses and clinical factors.
• Incorporated patient autonomy through tailored education and a patient-initiated consultation pathway.
• Alert escalation was mediated by alert thresholds, history, and clinical urgency.

Conclusions:

• The Ned algorithm can facilitate high-ratio nurse-led PCa survivorship care models.
• It offers a defined escalation pathway for urgent symptoms while respecting patient preferences.
• Anticipated benefits include improved decision-making, care continuity, and patient self-management.