How today’s automated healthcare scheduling sets the stage for AI [Webinar recap]

Across Canada, healthcare organizations are facing a perfect storm: staffing shortages, rising demand for services, and the growing complexity of scheduling under strict collective agreements. 

But let’s be honest… This isn’t exactly breaking news. These issues have been mounting for over a decade. 

What’s new is the spotlight on AI. With tools like ChatGPT making headlines, many are placing high hopes on artificial intelligence to solve healthcare’s toughest problems. But what can AI really fix when it comes to scheduling? And more importantly, is it the right conversation to have when so many organizations are still relying on paper processes?

That’s exactly what we explored in ourJune 25, 2025 webinar, hosted in partnership with the Long Term & Continuing Care Association of Manitoba (LTCAM). Here’s a full recap of that session. 

(You can watch the recording here.) 

Understanding the role of AI in healthcare scheduling 

Before diving into solutions, it’s important to clarify what AI actually means in healthcare operations. According to Mckinsey & Company, AI is a machine’s ability to perform some cognitive functions we usually associate with human minds, and there are two broad types:

1. Traditional AI (automation and rule-based intelligence)
   This type of AI performs structured, rules-based tasks such as analyzing data, identifying patterns, and automating decisions. It applies logic to help humans make faster choices. In scheduling, traditional AI can determine who is eligible for a shift, send offers, prioritize responses, and assign work without manual input.
   
   
2. Generative AI (creative and predictive intelligence)
   This is the AI people often associate with tools like ChatGPT. Generative AI can draft text, suggest actions, or produce content based on prompts. While it holds promise in healthcare, particularly for documentation or planning, it comes with higher complexity, implementation costs, and governance or security concerns.
   
   

Knowing when AI provides value

Healthcare leaders should avoid falling into the trap of seeking AI solely for its data analysis or predictive capabilities. For instance, knowing that absenteeism may rise tomorrow isn’t helpful unless the system can also recommend who to call, send out offers, or trigger an escalation.

That’s why, when evaluating new technologies, leaders should prioritize solutions that are embedded in real workflows and designed to support execution. In times of staffing pressure, the goal isn’t just to know what’s happening, but to act on it quickly.

How Traditional AI supports healthcare scheduling

A clear example of Traditional AI at work is automated shift call-out, a feature already in use at many healthcare facilities using the LGI Workforce Pro platform.

Traditionally, schedulers spend large portions of their day making individual phone calls to staff to fill vacant shifts (especially those caused by last-minute absences, leaves, or vacations). Automated shift offers streamlines the entire call-out process by generating a list of eligible employees based on collective agreement rules and sending shift offers via a mobile app. Here’s how it works: 

1. The scheduler sends a shift offer to all employees on its list.
2. The order in which employees are contacted is based on collective agreements and other rules.
3. Employees can accept or decline the offer with a simple swipe of the finger.
4. The scheduler may assign the shift to the employee with the highest priority.
5. The system automatically assigns the shift to the most eligible responder, updates payroll, and notifies all parties.

In the process described above, because the system analyzes data, applies predefined rules, and makes decisions without human intervention, it meets the definition of Traditional AI. 

The value provided by automated shift call-out in healthcare

During the webinar, we provided a practical scenario to quantify the value of shift call-out automation, a clear example of a machine performing cognitive tasks traditionally handled by humans.

To calculate the associated ROI, the following scenario was presented:

• A mid-sized facility with 300–400 employees.
• 50 shifts to fill daily.
• 7 calls required per shift.
• 15 minutes average wait time.
• $24/hour average scheduler wage.

By automating this one process, our sample organization can save at least 500 hours per month. This represents about $12,000 in monthly cost savings, or $144,000 annually, and it doesn’t account for reduced stress and improved staff responsiveness.

In healthcare scheduling, AI can’t provide value without the right foundation 

Even the most sophisticated AI can’t deliver results if it’s not grounded in the operational realities of the organizations it serves. This is why LGI spent two full years building and refining a collective agreement rules engine before layering in any automation. 

Without this kind of foundational work, automation may introduce errors, spark grievances, and erode trust among staff. When choosing scheduling tools, healthcare organizations should partner with providers who deeply understand the unique challenges of their environment, which is only possible when the provider is fully dedicated to healthcare.

To be ready for AI, build your foundations today. 

Whether you're juggling schedules at a hospital, LTC home, or regional network, automation and AI can reduce stress and boost efficiency. 

If you missed the live session, we invite you to connect with our team by booking a LGI Workforce Pro demo.