Humber River Health
Creating Human-Centered AI Ecosystems in Healthcare
Dr. Justin Grant
Dr. Justin Grant is a leader at the intersection of science and healthcare transformation. From helping build pioneering drug delivery research at the University of Toronto to driving breakthrough commercialization and start-ups at UHN, he has consistently turned bold ideas into tangible impact. Blending scientific rigor with business acumen, he advances Humber River Health’s nationally recognized innovation platform, shaping a future where research translates into better care and stronger health systems.
Scaling AI Through Collaboration with Clinicians and Patients
My approach at Humber is grounded in a combination of research experience, scientific rigor, entrepreneurial leadership and strategic system-level thinking. Humber River Health already has a strong digital foundation, being North America’s first fully digital hospital, with demonstrated success in areas such as its Command Centre, robotic surgery and AI-driven emergency department optimization.
Rather than viewing ambient AI tools in isolation, I see them as part of a broader strategy tied to Humber’s living lab, which includes the input from front line clinical teams and patients to aid in the development, adoption and transformation of care. My focus is on creating practical, clinically integrated innovation platforms where digital tools like AI scribes can be deployed, tested and scaled in real-world environments through collaboration with clinicians, academia and industry.
Measuring AI Impact Through Accuracy and Care
At Humber River Health, success for an AI medical scribe means freeing up clinicians from time-consuming paperwork so they can spend more time with patients. This would provide faster, more accurate notes, fewer after-hours charting demands and overall improved quality of care. Successful implementation of the tool will be measured by how well it reduces documentation time, improves note accuracy, and enhances staff satisfaction.
Safeguarding Care in the Age of AI
A common blind spot is assuming clinical teams will adapt to your tool, they won’t. Your solution must fit seamlessly into existing workflows and be flexible enough to work across specialties. Health systems also need clear evidence of ROI, so tracking outcomes like time and cost savings, documentation accuracy and clinician satisfaction are essential.
Our top concerns are keeping patient information safe and preventing dangerous errors or the misinterpretation of data. AI-driven documentation tools offer powerful efficiencies, but they also raise serious concerns around patient safety, data privacy and algorithmic bias. Inaccurate or incomplete notes can lead to clinical errors, especially if over-relied on without verification. Privacy risks emerge when sensitive patient data is shared with third-party systems, potentially breaching consent or regulatory compliance. Most critically, if AI is trained on non-representative data, it can embed and amplify bias leading to inequities in care and documentation. It is important that the tools we use work well for all patients regardless of their language, background or communication style. As adoption grows, ensuring transparency, accountability and rigorous safeguards is essential to protect both patients and healthcare providers.
Bridging the Gap Between Academic IP and Market Readiness
I have seen a shift toward more cautious, structured and proactive commercialization efforts. At UHN, I worked closely with startups and industry to support early-stage validation and data generation. There is now a stronger shift towards better models to prove efficacy, robust safety and human data for collaborators and investors to engage. At TIAP, I specialized in transforming early academic IP into viable companies by building investment materials, developing business plans, and driving funding strategies. A more global investment lens has led to successful funding outcomes and scalable ventures.
I emphasized bridging the gap between academic discovery and clinical implementation by building partnerships, securing resources and ensuring ventures were guided by scientific rigor, clinical practice insight, product development speed and commercial viability. My experience in forming successful innovation facilities, programs and startups highlights the impact in aligning innovation with tangible business and measurable healthcare outcomes.
Embedding AI Scribes Seamlessly into Care Workflows
If you're building an AI medical scribe, start with real-world demand from potential customers. Partner early with clinicians, EMR vendors and health systems to understand their pain points. A common blind spot is assuming clinical teams will adapt to your tool, they won’t. Your solution must fit seamlessly into existing workflows and be flexible enough to work across specialties. Health systems also need hard evidence of ROI, so track outcomes like time and costs saved, documentation accuracy and clinician satisfaction. Be proactive about data privacy, algorithmic bias and speech recognition across diverse users. Finally, don’t overlook change management, success hinges on trust, training, and usability, not just good tech!
Key Advice for Aspiring Leaders
My current work at Humber is focused on building centers of Innovation, beginning with emergency medicine, with plans to expand into surgery, ageing and digital biomarkers. I see academic and industry partnerships along with investor and donor support as critical to advancing healthcare innovation, particularly as government grants become more competitive. My strategy is to bring together institutional leadership, strategic funding models, and industry partners to build a powerful innovation ecosystem that delivers measurable improvements in patient care.
