Technical Implementation of a Decision Tree for Pain Entity Identification

Chronic pain places high demands on nursing care, while time constraints, extensive documentation, and limited access to pain specialists hinder consistent, evidence-based pain management. This study developed a backend decision logic component for a clinical decision support system for chronic pain management. An expert-designed decision tree was implemented as structured JSON rules executed by a JSON rule engine within an existing frontend prototype. The resulting system enables pain entity identification and was validated using expert-derived case studies. The results indicate that a rule-based backend is a feasible approach to support clinical decision-making in chronic pain management.