Photo caption: Consortium with Humber River Health, Niagara Health, SE Health, Seafair, Cambridge Memorial Hospital and Deloitte.
Health care systems across Canada continue to face growing pressure from patient flow challenges, prolonged hospital stays, and increasing demand for acute care beds. A key contributor to these pressures is Alternate Level of Care (ALC), a designation used when a patient no longer requires the intensity of services provided in a hospital setting but continues to occupy a bed while waiting for care elsewhere. Across Ontario, more than 80 per cent of ALC designations involve adults aged 65 and older, highlighting the complex care and transition needs of an aging population.
At Hennick Humber Hospital (HHH), an artificial intelligence (AI)-enabled initiative is taking a proactive approach to ALC by supporting earlier identification of patients who may experience complex transitions and enabling care teams to begin planning sooner. This work is about more than patient flow - it is about improving access to the right level of care, strengthening system capacity, and ensuring hospital resources are available for patients who need them most.
Through investment from Scale AI, HHH is working with Deloitte, Niagara Health, Cambridge Memorial Hospital, Seafair and SE Health to enhance iPlan, HHH's digital discharge planning platform, with advanced predictive capabilities. The goal is simple but innovative, identify patients at risk of becoming ALC earlier in their stay and support timelier and more focused coordinated discharge planning.
The need for innovation in discharge planning is especially urgent in Northwestern Toronto; the community the hospital serves. The region has the highest number of seniors over the age of 80 in Ontario, a figure expected to double by 2036. At the same time, 23 per cent of seniors live alone, many residents face complex socioeconomic challenges, and the area has long had one of the lowest rates of primary care attachment in the province. With 60 per cent of residents identifying as racialized, 54 per cent as immigrants, and higher-than-average rates of material deprivation, care transitions are often influenced by factors that extend well beyond a patient's immediate medical needs. These realities highlight the importance of earlier, more coordinated planning that takes these aspects into consideration.
The Pathway
The enhanced iPlan system uses AI-powered predictive machine learning models to analyze clinical and demographic data in real time, beginning at inpatient admission. These models generate early risk signals that help care teams anticipate which patients may face complex transitions out of hospital, suggest most likely discharge destinations, and group patients with similar care needs to inform earlier planning discussions. Patients with higher risk profiles are flagged sooner, triggering earlier involvement from physicians, nurses, social workers, allied health professionals, and Ontario Health atHome partners.
By shifting discharge planning further upstream, teams can begin parallel planning days earlier rather than waiting until the patient reaches medical stability. This improves alignment between patient needs and available services and helps prevent extended hospital stays that can impact both patients and the broader system.
“Innovation in health care must deliver both efficiency and compassion," says Beatrise Edelstein, Vice President, Post-Acute Care and Health Systems Partnerships at Humber River Health. “By embedding AI into iPlan, we are giving our teams the insight to anticipate patient needs and plan transitions with greater precision. This project will not only improve patient flow but also contribute to a more connected, efficient, and sustainable healthcare system across Canada."
At the heart of the initiative is a holistic approach to understanding patients beyond their immediate diagnosis. The predictive models consider a wide range of factors that can influence discharge readiness, including mobility status, previous admissions, social supports, and other social determinants of health. This broader lens enables care teams to identify potential barriers early and connect patients to the right services sooner, whether that means returning home with supports, transitioning to rehabilitation, or moving to another care setting.
“This is about bridging the digital and clinical worlds in a responsible way," says Gagan Grewal-Sagoo, Transformation Lead, Innovation Excellence Team at Humber River Health. “By bringing together clinical teams, operational leaders, and technical partners, we are building something that is clinically meaningful, operationally embedded, and structured for responsible, real-world use."
Development and Collaboration
Collaboration has been essential to the project's development. Representatives from across the patient journey, including allied health, social work, nursing, physicians, information systems, privacy and ethics teams, as well as executive leadership, have contributed to shaping how the tool works in real clinical environments. Health system partners beyond the hospital, including home and community care providers, have also been engaged to ensure the solution reflects realities across care settings.
“Having multiple disciplines involved ensures the model reflects what actually happens on the unit and in the community," says Nadine Sriskantharajah, Professional Practice Leader and Social Worker. “The goal is not just to predict risk, but to make sure the information leads to practical, timely actions that improve the experience and overall wellbeing of patients."
Pilot Progress
The iPlan pilot launched in July on two acute medicine units. Bringing the tool into daily practice required a coordinated effort across clinical teams, leadership, IT, Deloitte, and project leads to integrate it into daily bullet rounds and discharge planning workflows. Physicians, nurses, social workers, allied health professionals, and operational teams helped translate the tool's insights into shared discussions about patients' discharge needs.
Throughout the pilot, feedback from the units guided ongoing improvements. Teams used Plan-Do-Study-Act (PDSA) cycles to address system issues, streamline workflows, and improve the user experience. Participants reported that the tool became easier to use over time and better supported their daily work. This experience reinforced the importance of continued clinical involvement as teams put the solution into practice.
Preliminary findings are encouraging, as the model was found to help identify patients who may benefit from proactive discharge planning, and its pathway recommendations showed value in anticipating patients' eventual care settings. Early referral patterns also suggest that teams are connecting patients with services sooner, including Ontario Health atHome and rehabilitation, before an ALC designation.
The pilot has strengthened interprofessional collaboration around discharge planning while highlighting opportunities for further refinement. Feedback from the units identified additional clinical information that could help improve pathway recommendations, which have performed differently across the pilot units. These lessons highlight the importance of using the model's insights alongside clinical judgment and continuing to refine the solution with input from those using it.
While these findings suggest progress toward the pilot's objectives, results vary between the units, and a longer evaluation period is needed to understand whether the early improvements in planning translate into sustained patient flow benefits.
Looking Ahead
The next phase will involve a full evaluation of the pilot, bringing together model performance, discharge planning outcomes, and feedback from participating teams. This work will guide further refinements and inform decisions about broader implementation across Hennick Humber Hospital.
Beyond HHH, the long-term vision is scalability. While the initial integration is within iPlan, the underlying approach is being designed so it can be adapted by other hospitals and health systems, even those using different digital platforms. By demonstrating how AI can be thoughtfully implemented into existing clinical workflows, the project offers a blueprint for responsible, practical AI adoption in healthcare.
The pilot's progress reflects the contributions of teams across clinical care, technology, and operations. Their experience is helping Hennick Humber Hospital understand how AI can support earlier, more coordinated discharge planning, and bring tomorrow's health care, today.