Perioperative Opioid Usage Quality Improvement [CDTS-PDF1] - Year two

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system. Such a system could risk-stratify children and adults before surgery, so that pre-habilitation and optimized analgesic combinations can be employed to reduce persistent post-procedural pain. Artificial intelligence-augmented systems will also give clinicians actionable feedback on their practice, so they can learn how to improve their care, reduce their patients’ risk further, and
help them to recover more quickly from their procedure.
Postdoctoral fellows will lead day-to-day project activities and spend significant time working with both our clinical sites (St. Paul’s Hospital and BC Children’s Hospital) as well as industry partners (Careteam and Xerus) who will benefit from their methods expertise, ability to collaborate with clinicians and academic researchers, design, implementation, and evaluation skills.

Intern: 
Michael David Wood
Faculty Supervisor: 
Matthias Görges
Province: 
British Columbia
Partner University: 
Program: