From Silos to Synergy: Mapping the Adoption and Interoperability Gaps of Clinical AI in Canadian Healthcare

Abstract

Artificial intelligence (AI) is increasingly integrated into clinical workflows worldwide, yet in Canada its adoption remains fragmented, unevenly distributed, and siloed within acute care and diagnostic imaging. To address the absence of a pan-Canadian view, Digital Health Canada's CHIEF Executive Forum convened the AI in Action Working Group, a national collaborative created to advance shared learning, interoperability, and responsible clinical adoption of AI. As Stage One of this initiative, the Working Group conducted the first structured environmental scan of clinical AI deployments across Canada to create a national baseline and inform future interoperable scaling. Using publicly available sources and a standardized taxonomy, 152 initiatives were identified across provinces and territories. For each entry, data elements (including venue, function, technology type, deployment stage, partnerships, and outcomes) were independently verified using a common coding framework. This taxonomy enabled semantic and analytic interoperability by allowing consistent comparison across jurisdictions and maturity levels. Four signals emerged: (1) workflow-embedded applications show the strongest adoption; (2) equity and interoperability gaps persist, with primary care, long-term care, community health, and Indigenous/remote settings underrepresented; (3) evidence reporting is minimal, hindering evaluative and organizational interoperability; and (4) large language models and robotics represent emerging clinical frontiers. This national scan demonstrates that Canada's clinical AI landscape is diverse but constrained by gaps in semantic, workflow, organizational, and evaluative interoperability. By establishing a shared national taxonomy and baseline dataset, this work creates foundational infrastructure to support coordinated, equitable, and interoperable adoption.

Ähnliche Arbeiten