Machine learning to improve clinical alarm management and patient safety
Featuring Dr. Aaron Conway
Thursday, April 28, 2022
12:00 pm – 1:00 pm
Zoom details will be sent to registrants
The need to diminish pain, alleviate anxiety, and optimize patient comfort during medical, dental, and surgical procedures has led to the worldwide practice of procedural sedation and analgesia. This talk will provide an overview of how Dr. Conway is using machine learning approaches to improve clinical alarm management and to make these procedures as safe and effective as possible. Dr. Conway’s approaches align with a call from The Society for Critical Care Medicine Alarm and Alert Fatigue Task Force, that machine learning techniques should be used to advance the quality of alerts that clinicians receive from physiological monitoring devices. By providing practical examples from his latest research, Dr. Conway will share broader insights into how predictions from machine learning models are generated.
Dr. Conway holds the RBC Chair in Cardiovascular Nursing Research at the Peter Munk Cardiac Centre (Toronto General Hospital) and is an Assistant Professor in the Lawrence Bloomberg Faculty of Nursing. Prior to joining UofT in 2018, Dr. Conway practiced as a Registered Nurse in the cardiac catheterization laboratory (CCL) setting in Australia. Dr. Conway’s research aims to make the procedural sedation experience during medical, dental and surgical procedures as safe and effective as possible.