PROJECT TEAM
Martin Hylleholt Sillesen, MD, PhD, Associate Professor of Surgery, Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Department of Surgical Gastroenterology, Center for Cancer and Organ Diseases, University of Copenhagen and Copenhagen University Hospital,
THE NEED
Up to 15% of surgical and trauma patients experience preventable complications such as infections, thrombosis, or organ failure. While AI models have shown promise in predicting these events using historical data, the lack of real-time EHR data transfer in Denmark prevents these models from being applied in clinical practice, delaying early intervention and limiting patient safety.
THE SOLUTION
The project establishes a secure, event-driven EHR data pipeline from the Danish healthcare platform (EPIC) to a cloud-based system, enabling real-time analysis by AI models. A pilot study will validate one of the team’s AI models, which predicts the risk of death or 18 types of postoperative complications. The system will deliver instant risk scores to clinicians, improving decision-making and patient outcomes.
Real Time Surgical Data - Rigshospitalet
Call 1 - 2022
500.000 DKK
Clinical Area
Surgery
Technology
HealthTech, AI
PROJECT SUMMARY
Real Time Surgical Data enables real-time integration of electronic health records with AI models to predict postoperative complications. By providing immediate risk assessments, the system supports timely clinical interventions to prevent infections, organ failure, and other adverse outcomes
CLINICAL IMPACT
Real-time AI predictions can help prevent avoidable complications by alerting clinical teams before critical events occur. This leads to fewer adverse events, shorter hospital stays, and improved survival rates. It also supports the integration of AI-driven decision support into everyday clinical workflows, paving the way for safer and more efficient surgical care.