Real-Time Surgical Data
Patients who undergo any medical treatment are at risk of treatment-related complications. In fact, more than 15% of surgical patients and those seeking treatment for injuries experience a potentially avoidable complication, such as infection, thrombosis or organ failure.
The Inspiration Behind the Innovation
Over 70% of postoperative complications could be prevented if advanced warning were available. At the current rate, these complications increase treatment costs in Denmark by over 200%.
Large datasets regarding post-operative complications already exist, and complication rates have remained consistent over the last decade. Leveraging this data in an AI-enhanced prediction model could help assess each patient’s risk of complications, enabling preventative measures while significantly reducing healthcare costs.
An upwards of 15% of surgical or injured patients experience avoidable complications, such as infections, thrombosis events or organ failure. This project uses AI-enabled analysis of real-time data (e.g. chart notes, lab results, vital-sign registrations) for individual patients to predict 18 different post-surgical complications, resulting in fewer complications, less use of medication and fewer repeat surgeries.
Martin Hylletoft: Staff Physician, PhD, Associate Professor of Surgery; Copenhagen University Hospital, Rigshospitalet
External advisors in technology and business development from Aiomic ApS.