A Systematic Literature Review of Success Factors for Digital Transformation in Ontario’s Healthcare System
Bibliographic record
Abstract
This research investigates the essential elements that drive digital transformation in Ontario's healthcare system through a systematic analysis of ten peer-reviewed articles from 2018 to 2025.The qualitative study employs the Technology-Organization-Environment (TOE) framework to analyze diverse healthcare settings including communitybased clinics, primary care and hospital systems.The researcher conducted targeted searches on Google Scholar and PubMed to select articles that focused on digital health implementation efforts in Ontario or offered transferable relevance.The research identifies recurring patterns in three main areas which include technological aspects like EMR usability, interoperability and organizational aspects including leadership involvement, staff education and environmental factors such as policy consistency and intersectoral teamwork.The review unifies evidence from various real-world settings to help healthcare planners; digital health leaders and policymakers create equitable and scalable digital strategies.The study establishes a thematic framework which will direct upcoming digital health initiatives throughout Ontario's changing healthcare environment.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.039 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".