Factors Influencing Patients' Satisfaction with AI-Guided Triage System: A Case Study of Longgang Central Hospital

Abstract

The integration of artificial intelligence (AI) into medical triage has shown promise in reducing waiting times and improving resource allocation. However, patient satisfaction with AI-guided triage systems remains underexplored. This quantitative case study at Longgang Central Hospital (Shenzhen, China) surveyed 387 adult patients who used the AI triage system. Multiple regression analysis examined how system functional factors (accuracy, efficiency, usability, privacy protection) and patient characteristics (age, gender, education, prior digital health experience) influence satisfaction. Results showed that all four functional factors positively predicted satisfaction, with efficiency having the strongest effect (β = 0.297, p < 0.001). Age negatively affected satisfaction (β = -0.102), while education and prior experience had positive effects (β = 0.085 and 0.118, p < 0.01). Gender was not significant. The findings provide actionable insights for optimizing AI triage systems in public hospitals.