Digital health transformation in Quebec: assessment of interoperability and governance strategies
Bibliographic record
Abstract
The rapid expansion of health data has led to unprecedented information availability within healthcare systems. Health information systems (HIS) play a central role in managing this data and enabling improvements in care delivery, system performance, and population health monitoring. Maximizing the value of HIS, however, requires effective information exchange across systems, making interoperability a critical prerequisite. Despite its recognized benefits, interoperability remains a major challenge within Quebec's Health and Social Services Network, largely due to the heterogeneity and fragmentation of HIS across healthcare institutions. This paper assessed how Quebec's Plan sante addressed interoperability challenges, using the dimensions from the Healthcare Information and Management Systems Society (HIMSS): foundational, structural, semantic, and organizational interoperability. This study highlighted initiatives aimed at strengthening infrastructure and information system architecture to support foundational interoperability and showed persistent challenges at the structural and semantic levels, particularly those related to the adoption of standardized data formats and harmonization of clinical terminologies. Finally, significant implementation challenges that require coordinated change management were identified regarding the organizational interoperability. Overall, while the Plan sante demonstrates a clear commitment to technological modernization, it does not fully address the interoperability multidimensional nature. Achieving meaningful interoperability will require sustained efforts across technical, normative, and organizational domains beyond the strategies currently outlined. Recent governance developments, including the creation of Sante Quebec, add complexity to this evolving context and raise further questions regarding the coordination of interoperability governance.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".