Policy context and digital development: a comparative study of trajectories in 4 Canadian academic health centers over 30 years
Bibliographic record
Abstract
OBJECTIVES: The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development. MATERIALS AND METHODS: AHCs play a leadership role within health systems in data-driven improvement. This retrospective case study draws on documentary, observational, and interview data to compare digitalization efforts over 3 decades in 4 AHCs in the province of Quebec (Canada). RESULTS: At system level, strategy shifted from supporting multilayered development that encouraged bottom-up initiatives in the first decade of the 2000s, to harmonizing clinical information systems in a highly prescriptive manner after 2010. AHCs experienced the shift differently according to concurrent impacts of health system restructuring, and internal choices around electronic health record (EHR) systems and implementation priorities. Digital maturity remained low in all 4 AHCs. DISCUSSION: Coordination between system strategies and organizational strategies in AHCs was neglected in early digital development in Québec and improved only after an intense period of prescription and resistance. Confrontation highlighted tensions around different objectives at AHC and system level, competing missions within AHCs, and trade-offs between relying on commercial EHRs and developing publicly owned systems, all of which ultimately influence EHR implementation. CONCLUSION: The different experiences of focal organizations with digitalization underline the importance of adapting national strategies and providing support to implementers, building on acquired strengths, and arriving at the right balance of guidance from the top and autonomy to develop innovative capacities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".