COULD SMART DATA FINALLY HELP CLEAR THE PACKED EMERGENCY ROOMS AND SURGERY WAITLISTS IN CANADA?
Emergency rooms across Canada are overcrowded. Waitlists for surgeries are getting longer. In order to get the care they need, patients have to wait for hours, days, or even months. Although the burden on the healthcare system is not new, many hospitals are turning to healthcare analytics to find answers as they struggle with rising demand and constrained resources.
Predictive analytics, a field that uses AI and machine learning to foresee patient demand and analyze hospital operations, is being looked into as a way to improve efficiency and reduce wait times. But how effective is it? And what are the challenges in implementing it in Canadian hospitals?
To learn more about this, the Fulcrum met with Christopher Sun, Canada Research Chair in Data Analytics for Health Systems Transformation at the University of Ottawa. Sun has spent years studying the intersection of optimization, artificial intelligence, public health, and health equity. While predictive analytics holds a lot of promise, Sun explains that adoption in Canadian hospitals is still in its early stages, with many challenges preventing widespread implementation.
Congestion in emergency rooms is one of the main problems facing Canada’s healthcare system. Before visiting a doctor, patients often have to wait for hours, and some even leave before receiving treatment. Using predictive analytics to anticipate ER demand before it occurs may help hospitals distribute resources more effectively.
Sun explains that two main types of analytics play a role in dealing with ER overcrowding. The first is predictive modelling, which uses AI to forecast patient volume, wait times, and patient outcomes. The second is operations research, which focuses on optimizing hospital decision-making, such as staffing levels and bed allocation.
To predict increases in ER visits, hospitals already use historical data, seasonal trends, and real-time admissions statistics. For instance, hospitals may predict a rise in the number of patients during flu season or bad weather. But prediction by itself is insufficient.
“Even with predictive modelling to estimate demand, if hospitals lack the staff and systems to respond effectively to increased demand, then we won’t see benefits from using such approaches,” Sun explains. Hospitals need both accurate data and the ability to act on it, a challenge that remains in many parts of the country.
Yes, overcrowding in the ER is the most visible problem, but a bigger crisis is happening behind the scenes: the growing backlog of appointments and procedures. A lot of people have to wait months or even years for treatments that might considerably improve their quality of life.
Healthcare analytics is helping hospitals improve how they schedule appointments and plan surgeries, and making sure operating rooms are used as efficiently as possible, with no time wasted. Sun compares the process to playing a giant game of Tetris, trying to fit procedures of different lengths into a fixed amount of space without wasting time or resources.
The better the fit, the more patients can be seen, and the shorter the wait times become. “Even the best analytical tools can be ineffective in practice without leadership buy-in, a culture of innovation, and champions for data-driven decision-making,” he explains.
But if predictive analytics has the potential to transform Canada’s healthcare system, why hasn’t it been fully adopted? According to Sun, several major challenges are slowing our progress. One of the biggest challenges in using predictive analytics isn’t just collecting data, it’s making it useful.
Hospitals may have electronic records, but if the data isn’t clean, complete, or standardized, it’s not much help. Add to that the shortage of data experts in healthcare (many head to tech companies instead), and you’ve got a talent gap.
And even if the data is solid and the analysts are in place, none of it matters without leadership buy-in. “Even the best analytical tools can be ineffective in practice without leadership buy-in, a culture of innovation, and champions for data-driven decision-making.”
Some hospitals in Canada are starting to make real progress with predictive analytics, showing that analytics-based approaches can actually work when done right. At the Ottawa Heart Institute, researchers are using AI to schedule operating rooms more efficiently, predict in-hospital cardiac arrests, and even speed up ECG analysis.
Meanwhile, at The Ottawa Hospital, a project led by a student made a noticeable difference, helping to smooth out ER logjams and making sure more patients actually got the care they came for. Christopher Sun says it’s this kind of teamwork, when clinicians, data experts, and hospital leaders come together, that really moves the needle.
“It takes a village to bring these models to fruition and see real improvements,” he says. The encouraging part? “Fortunately, more groups are starting to realize the potential, and we’re seeing more investment in this space.”
The bottom line is that predictive analytics won’t fix Canada’s healthcare crisis overnight, but it has the potential to make the system more efficient, reduce wait times, and improve patient outcomes.
The real challenge lies in turning research into action, making sure that hospitals not only develop predictive models but also have the resources and willingness to implement them effectively.
