Optimizing post-procedural pain trajectories through patient-oriented research and artificial intelligence

Principal Investigator: 
University: 
Provincial Health Services Authority
Award Type: 

Our goal is to use smartphones and artificial intelligence to improve pain management for children having surgery. This is needed because many children still have a lot of pain even a year after surgery. The pain affects their daily life, and might cause them to return to hospital. A child’s pain is affected by many things, like their biological sex, anxiety, coping skills, pain level, and type of surgery. Importantly, some of these can be altered.

We will collect data to identify patterns that predict which children

  1. do well after surgery, so we can learn from them or
  2. do not do well/have significant pain, so we can help sooner or even prevent it. We will involve families and children having surgery now, to collect data for a pain risk score to help future children.

We will design a tool to share pain risk data with families and doctors and test these tools in children coming to hospital for spine, tonsil or dental surgery. We hope that using these tools (pain prediction models) will improve the child’s individual care. Identifying children at high pain risk will allow us to intervene before their surgery. This will lead to quicker recovery, less time in hospital, and less chance of addiction to painkillers (opioids).

Research Pillar: 
Host Institution: 
Provincial Health Services Authority
Research Location: 
BC Children's Hospital Research Institute
Year: 
2020