关键词: 骨科；围手术期；疼痛护理管理；临床决策支持系统；闭环管理

Abstract: Objective: To assess the application effects of an intelligent decision support system for perioperative pain management in orthopedic patients, provide a reference for improving the quality and efficiency of decision support. Methods: We developed a clinical decision support system based on a knowledge base for perioperative pain management in orthopedic patients. The system supported multidimensional pain assessment, pain early warning and identification, rule-based reasoning, and decision analysis. Totally 339 orthopedic inpatients in a tertiary grade A hospital in Nanjing from October to December 2022 were recruited as the control group, and 330 orthopedic inpatients from January to March 2024 were recruited as the experimental group. The control group received conventional pain care management, while the experimental group was managed with an intelligent decision support system for perioperative pain management in orthopedics. The first pain assessment rate, re-assessment rate of moderate to severe pain, timely pain management rate, incidence of moderate to severe pain, average time of first postoperative ambulation, satisfaction of nurses and patients, and average hospital stay were compared between the two groups. Results: The first pain assessment rate in the experimental group was 99.1%, and that in the control group was 85.3%, with no statistically significant difference between the two groups (P>0.05). The re-assessment rate of moderate to severe pain in the experimental group was 96.1%, and the timely pain management rate was 97.9%, which were higher than those in the control group. The incidence of moderate to severe pain in the experimental group was 22.1%, the average time of first postoperative ambulation was 22.79±1.26 hours, and the average hospital stay was 6.50±0.90 days, all of which were lower than those in the control group. The satisfaction scores of nurses and patients in the experimental group were higher than those in the control group, and the differences were statistically significant (P<0.05). Conclusion: The intelligent decision support system for pain management effectively improves the closed-loop management quality of perioperative pain in orthopedic patients, enhances the work efficiency of nurses, reduces the incidence of moderate to severe pain in patients, enables early postoperative ambulation, and increases patient satisfaction.

Key words: orthopaedic; perioperative period; pain management; clinical decision support system; closed-loop management