A hybrid AHP and K-means model for biopsychosocial surgical prioritization: validation in a high-complexity ENT unit

• AHP and K-Means enable prioritizing surgical patients using biopsychosocial criteria. • Expert knowledge and clustering identify clinically relevant patient groups. • Structured prioritization boosts clinical outcomes and operational efficiency. • Model validation shows significant improvements in clinical risk, hospitalization rates, and waiting times for high-priority patients. To address the critical challenge of efficiently and ethically managing surgical waiting lists in digital health systems by developing a decision support framework based on biopsychosocial prioritization. The authors integrate the Analytic Hierarchy Process (AHP) with K-Means clustering to create a hybrid decision support model that prioritizes patients using multidimensional biopsychosocial variables. The model was applied in the otolaryngology (ENT) unit of a high-complexity public hospital in Chile. Expert-informed weightings guided the AHP process, while K-Means clustering enabled data-driven segmentation into clinically coherent patient groups. The proposed methodology significantly outperformed traditional chronological scheduling approaches. Specifically, it achieved a 27 % reduction in mean clinical risk, a 41 % decrease in urgent hospitalizations, a 32 % reduction in urgent bed days, and more than 12-days of acceleration in access for high-priority patients. While the AHP-clustering hybrid is established in prior literature, our contribution lies in operationalizing it with ethical safeguards and real-world validation within a high complexity ENT unit. Our hybrid AHP and K-Means approach offers a transparent, scalable, and interpretable decision support tool for surgical prioritization. It aligns with the goals of digital health transformation by improving the fairness, efficiency, and responsiveness of healthcare delivery.