Updated: Dec 4, 2020
Original author: Dianne Daniel
Published: October 29, 2020
Consider the following urgent situations:
Concerned parents rush their child to The Hospital for Sick Children (SickKids) emergency department in Toronto. After checking in with the triage nurse, they go to the waiting area and a short while later, they receive a message that a urine test and X-ray have been ordered and are told what to do next. By the time they see the physician, she’s already armed with the results and makes an informed decision about their child’s care on the spot.
The condition of an inpatient at St. Michael’s Hospital in Toronto starts to deteriorate, putting him at high risk for an adverse event. A secure email is immediately sent to the nurse’s desk and the attending physician receives a page, allowing for early care intervention and preventing a sudden trip to the ICU.
A critically ill infant in the neonatal intensive care unit (NICU) at Southlake Regional Health Centre in Newmarket, Ontario, starts to develop sepsis, a condition that appears suddenly and can turn fatal within hours. Clinicians are alerted and quickly intervene with life-saving measures.
The common thread between all three of these research scenarios, currently under way in Ontario? A branch of artificial intelligence called machine learning that is poised to reshape medicine, one predictive model at a time.
“This is a major data and computing revolution of our time,” said Dr. Amol Verma, an internist and scientist at St. Michael’s. “If we’re going to figure out how to harness it for healthcare, to really improve how we deliver care for our patients, it’s going to take a lot of hard work.”
Dr. Verma is part of a multi-disciplinary team at the hospital working on an early warning system called CHARTwatch, designed to reduce mortality and improve the quality of care of patients on the general internal medicine ward. One of the first Canadian hospitals to establish an in-house data science and advanced analytics team – including a multimillion-dollar infrastructure investment and the creation of a vice-president of Data and Analytics position – St. Michael’s recognizes machine learning as a “key area of healthcare development and innovation,” he said.
In simple terms, machine learning is the process of using algorithms to teach a computer to make accurate decisions and predictions based on data. The goal of CHARTwatch is to improve real-time clinical decisions by automating the process of rapidly collecting and analyzing data from the hospital’s electronic medical record (EMR).
Whereas similar predictive models, widely used in the U.K., were designed to analyze five to 10 data points and use simple statistical algorithms, CHARTwatch analyzes more than 100 data elements stored in the hospital’