Machine Learning-Driven Prediction of Significant Postoperative Pain in Patients Using Intravenous Morphine Patient-Controlled Analgesia

Pain is a common problem after gynecological operations, yet hospitals often struggle to predict which patients will need extra attention. In this study, we aimed to improve how doctors identify patients who are likely to experience severe pain after surgery. We used information already collected in electronic medical records to build computer models that estimate pain risk early to enable care teams’ faster responses by the care teams. By analyzing data from 4,000 women who had gynecological surgery and received patient-controlled pain relief, we tested several machine learning methods and found that two models performed best by correctly identifying high-risk patients more than three out of four times. The most important signals included early pain patterns, how often patients pressed the pain relief button, and details about the surgery. We also designed a dashboard to show these risk scores during routine pain rounds to help doctors make timely decisions without replacing their judgment. This approach could lead to better recovery, fewer complications, and smarter use of hospital resources.