Using artificial intelligence to optimize care delivery systems

While AI headlines often focus on diagnostics or drug discovery, many opportunities lie behind the scenes in operational decision-making to improve efficiency and access to care for Canadians. AI can support smarter decision-making by surfacing patterns across complex datasets, predicting the impact of proposed changes both locally and system-wide, and factoring in broader system dynamics to guide more informed, effective actions.

Here are three practical ways AI is being applied in care delivery optimization:

1. Simulating complex systems: Hospitals have long tried to improve patient flow through initiatives like discharge planning, care transition programs, and surge protocols. While valuable, these efforts are often designed in silos or trialed without fully understanding their ripple effects. AI-enabled simulations and digital twins change that, using real operational data to map how patients move across departments, from the Emergency Department to inpatient units to discharge destinations. These models can test how interventions like earlier discharges, expanded transitional care capacity, or additional ED physicians at peak times affect system-wide performance. Leaders can explore “what if” scenarios and identify where targeted changes will unlock the greatest improvement, whether reducing ED crowding, ALC days, or surgical bottlenecks, before implementing changes in the real world.
2. Matching demand and capacity: Surgical scheduling is a complex multi-factorial puzzle. With expensive OR resources, limited staff, and urgent patient needs, every decision about who gets booked when has downstream effects on wait times, overtime, and efficiency. Traditionally managed manually with paper booking packages, spreadsheets and estimates, scheduling often underuses available capacity or causes bottlenecks. AI Models trained on historical case durations, turnover times, and current waitlist characteristics can dynamically build optimized surgical slates, proposing the best mix of procedures and resources to maximize utilization while minimizing delays. This is not just about booking slots; it’s about strategically aligning surgical demand with real-world capacity constraints. Similar models can also be applied outside the OR. For example, to align inpatient discharge planning with ALC bed availability, or to match ED staffing with real-time triage volumes, ensuring scarce resources are deployed where they’re needed most.
3. Surfacing hidden patterns at pace: Given the large amounts of data produced in healthcare, it can be a laborious process to analyze, data to uncover patterns and identify opportunities for improvement. An area ripe for this is reducing surgical supply variation where AI models can analyze thousands of surgical cases to uncover cost differences between providers and opportunities to reduce spend through standardization or lower-cost alternatives, surfacing patterns not visible through manual review.

These tools build on data that most hospitals already collect, including EMR records, operating room logs, supply chain data, and administrative datasets. With AI, we can connect these dots faster and use those insights to drive change.

While the focus of this article is on AI’s role in operational excellence, it is worth noting the growing interest in AI tools that support frontline care delivery. Solutions like AI scribes are already reducing administrative burden for clinicals by transcribing notes in real time, and emerging AI tools, such as Hippocratic AI, are being tested to communicate with patients directly to provide pre-operative instructions or post-discharge follow-up. These tools represent an exciting frontier and underscore the broader transformation AI is bringing to all corners of healthcare.

The promise of AI in healthcare is compelling, but to unlock its full value, organizations must focus as much on how AI is used as where it's applied. AI tools can generate powerful insights, but they won’t drive change unless embedded into day-to-day operations. That means rethinking workflows, adapting team roles, and building confidence in data-driven decision making. Organizations must adapt existing workflows, clarify roles, and equip teams with the training and tools needed to use AI effectively.

AI doesn’t replace people; it augments their ability to act faster and more effectively. But realizing that potential requires thoughtful change management, cross-disciplinary collaboration, and practical implementation strategies rooted in real operational contexts.

Organizations looking to integrate AI into operations don’t need to start with large-scale transformation. In fact, some of the most impactful changes begin with existing priorities: improving flow, reducing variation, or optimizing resource use; supported by new tools and new thinking.

Here are four guiding principles for teams ready to explore what AI can do:

• Start with a real problem, not an AI project: Focus on persistent operational challenges like OR inefficiency, long stays, or schedule gaps, and explore how AI might offer a new lens or faster insight.
• Redesign processes, not just plug-in tools: AI models can predict, flag, or recommend but their value depends on how teams use those insights. Be prepared to adjust workflows and embed AI into the rhythm of decision-making.
• Support the people who will use it: Train staff, clarify roles, and build trust in the tools. Empower frontline teams to participate in design and feedback.
• Learn from others and pilot thoughtfully: Use targeted pilots to test new ideas before scaling. Draw on lessons from peer organizations to accelerate adoption and avoid common pitfalls.

The message is clear: AI isn’t replacing people, it’s accelerating insight. In a system that can no longer afford to stand still, that kind of momentum matters. With the right foundation in place, AI can become a powerful ally in the next era of operational excellence. We’re ready to partner with you, share our tools, and support your planning journey ahead.