AI Could Change How Ottawa ERs Handle Chaos
Queensway Carleton doctor tests machine-learning triage. Early wins suggest faster patient placement could ease bottlenecks.
Walk into most emergency rooms around 10 p.m. on a weeknight and you'll see the same scene: charts piling up, patients waiting hours in the waiting room, doctors moving between cases with barely a moment to breathe. At Queensway Carleton Hospital, Dr. Gautam Goel stares at this problem every night shift. Fifteen patient charts waiting. Triage decisions that ripple across the entire evening.
He started thinking differently about those bottlenecks when he realized that machine learning might cut through the noise. Not to replace doctors—but to organize the chaos.
The idea is elegant: AI tools could flag incoming patients, suggest optimal placement based on severity and available resources, and surface patterns in wait times that human eyes miss in the rush. Early pilots at QCH suggest the system actually works. Patients get routed faster. The ER doesn't back up as quickly. Doctors spend more time treating, less time hunting for the next available bed.
Ottawa's healthcare system is already stretched thin. The city's population keeps growing, bed counts haven't kept pace, and the pandemic left scars that won't heal quickly. A tool that even modestly improves efficiency could mean less time people spend in pain, waiting.
None of this replaces the doctor's judgment—but it might buy them the minutes they need to actually use it. That's the gap AI could fill.