A consortium of 11 health institutions, including The Ottawa Hospital, the CHUM in Montreal, and Princess Margaret Cancer Centre in Toronto, partnered with Gray Oncology Solutions on a project supported by ScaleAI to tackle inefficiencies in cancer care operations. Built on GrayOS, the Care Orchestration Platform, the project demonstrated how Artificial Intelligence (AI) can unlock measurable gains in efficiency, staffing, and access to care.

Cancer treatment is among the most resource-intensive areas of healthcare, where manual scheduling often wastes capacity, creates staffing pressures, and delays access to care. To address these challenges, a pan-Canadian consortium was formed, bringing together The Ottawa Hospital, Princess Margaret Cancer Centre, the CHUM, Gray Oncology Solutions, the Jewish General Hospital, the Programme Québécois de Cancérologie, the Centre intégré de santé et de services sociaux de Laval (CISSS), the McGill University Health Centre (MUHC), the CIUSSS de l’Estrie – Sherbrooke, Southlake Regional Health Centre, and the Canadian Cancer Society. Together, the consortium developed and deployed advanced AI tools to predict demand, anticipate cancellations, recalibrate appointment times, and support smarter rescheduling.

About the AI Technology

The project built new capabilities on GrayOS to directly support frontline operations:

• Appointment duration prediction in radiation therapy: recalibrating treatment times to ensure schedules reflect true patient needs.

• Patient flow prediction in radiation therapy: forecasting demand to plan resources more effectively.

• Dynamic rescheduling engine (“Concierge Mode”) in radiation therapy: automatically clustering appointments and re-optimizing schedules when disruptions occur.

• Cancellation prediction in systemic therapy: anticipating missed appointments in infusion clinics to inform pharmacy preparation and nurse staffing.

• Overbooking recommendations in systemic therapy: using prediction models to fill likely cancellations and reduce wasted chair time.

These tools enabled hospitals to plan proactively rather than reactively, aligning patient demand with staff, equipment, and resources.

Key Results

• Expanded patient access: Efficiency gains translate into capacity to treat nearly 1,000 additional patients each year across participating centres.

• Financial impact: ~$800,000 in annual realized savings per centre, with potential to reach over $1.1M annually as adoption matures.

• Staffing benefits: Improved pharmacy and nursing alignment, reduced rescheduling burden on senior staff, and smoother workload distribution.

• Workflow improvements: Better visibility into cancellations and appointment durations, enabling more proactive and reliable planning.

These outcomes show how AI can improve the operational backbone of cancer care, helping hospitals make the best use of their precious resources while improving staff experience and patient access.

For the full report, or to learn more about GrayOS, please get in touch with us.