From AI Scribe to AI care partner: Real clinical impact today, augmented clinicians and unlocked capacity tomorrow

Sponsored Session

Room: 201 ABC

Canadian medicine was designed around scarcity. Not enough time, not enough clinicians, not enough resources. Almost every workflow in place today was built to compensate for that constraint, and most of them spend the resource already in shortest supply. That isn’t care. It’s compensation for system constraints.

Dr. Sameer Shaikh starts with what has already changed. Across five three-month Ontario pilots and 111,770 documented visits, Heidi supported an average 13 minutes of documentation per visit and 25,098+ clinical hours across care teams. Michael Garron’s Stavro ED is seeing 10 to 13% more patients per physician per shift. Cambridge North Dumfries OHT sees 11 minutes of documentation supported per session, with 90% reporting reduced stress and burnout. The Ottawa Institute of CBT cut documentation from 213 minutes a week to 71. Real sites, measured results, Canadian conditions.

Next: augmented and agentic care. Good clinical care is not only a documentation problem, it depends on informed decisions. Heidi Evidence gives clinicians access to trusted medical research at the point of care, grounded in guidelines and clinical literature, with no commercial influence on what is shown.

Heidi Remote, a clip-on mic built for clinical use, lifts the work off the screen and puts you back across from the patient. Heidi Routines take on the task behind the note: the referral, the form, the order, the follow-up. Each one drafts a disposition with its reasoning and source line, then stops. Nothing writes back to the EMR without clinician approval. Capacity comes from work that arrives already done, waiting on a decision.

Safety, privacy and security are not the disclaimer slide at the end of an AI talk. They are the reason a clinician trusts the output at all. Dr. Shaikh covers how Heidi is built to support compliance with PIPEDA and applicable provincial privacy and health-information laws including PHIPA, how patient health information for Canadian deployments is stored exclusively in Canada, how outputs are checked four ways rather than asserted, and why the augmented clinician of tomorrow only arrives if today’s tool takes no shortcuts.

Heidi is a Qualified Vendor under Supply Ontario’s Artificial Intelligent Solutions, AI Scribe Vendor of Record arrangement.

Learning objectives

By the end of this session, participants will be able to:

Evaluate an agentic clinical workflow against four safety requirements: human approval before any EMR write-back, claim-level traceability, continuous rather than sampled scoring, and named accountable roles

Interpret measured results from five Ontario pilot sites to judge what documentation load is realistically recoverable in their own setting

Distinguish four modes of working with clinical AI (microtask, copilot, delegate, partner) and place their current workflows against them

Speaker:

• Sameer Shaikh, MD, FRCPC (Emergency and Critical Care), MAIHC, DRCPSC (CE), Canadian Clinical Director, Heidi

Presenter Bio:

Sameer Shaikh, MD

Dr. Sameer Shaikh is an Emergency and Critical Care physician in Ontario with a focused interest in advancing the safe and practical integration of artificial intelligence into frontline clinical care. He serves as a Royal College Clinician Education and has completed a Master’s degree in Artificial Intelligence in Healthcare, with academic and operational work centered on bridging the gap between emerging AI capabilities and real-world clinical workflows. Dr. Shaikh regularly delivers keynote talks and workshops to clinicians, health system leaders, and industry partners on applied AI, change management, and responsible adoption in healthcare. He has led the development and implementation of AI-enabled care gap closure and workflow optimization platforms designed to support physicians and physician extenders in identifying high-risk patients, reducing documentation burden, and improving evidence-based care delivery. His work focuses on translating AI from theory to bedside impact—enhancing efficiency, strengthening clinical decision-making, and ultimately improving patient outcomes.

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