Navigating AI-Driven Digital Transformation in Canadian Healthcare: The Case of CHUM’s AI School
Bibliographic record
Abstract
AI-driven digital transformation (AIDT) plays a crucial role in enabling organizations to adapt to the rapidly changing landscape of digital innovations and modern technologies. However, it also brings forth its own array of challenges which are even more pronounced in hospitals, which often operate in crisis mode, where the margin for error must be minimal, even amidst resource constraints and unforeseen pressures such as the COVID-19 pandemic. Hence, the question arises: How does a high-demand context such as a hospital facilitate its AIDT amid the pressures of constant patient care and evolving medical technologies? To address this question, we have been conducting an in-depth case study by examining CHUM’s School of AI in Healthcare (SAIH). By studying its approach, challenges, and functions, we aim to provide insight into navigating AIDT in healthcare. Upon completion of the research, we will provide actionable recommendations that can be implemented in similar high-pressure contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.063 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".