Unlocking the potential: Responsibly embracing artificial intelligence to advance the use of health data and analytics at the Canadian Institute for Health Information
Bibliographic record
Abstract
Canadian Institute for Health Information (CIHI) is looking to modernize and adopt new ways of working. This incudes the use of new technology, including the application of Artificial Intelligence (AI). To begin in a purposeful manner, the organization developed an AI strategy which was informed through feedback from key stakeholders and partners, from its staff and from a review of international research. The research informed several ways AI could add value to CIHI's internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems across Canada. This article describes the strategy development process and the areas of focus within the strategy.
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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 | 0.224 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.033 | 0.088 |
| Scholarly communication | 0.049 | 0.018 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".