Users' perspectives of barriers and facilitators to implementing EHR in Canada: A study protocol
Bibliographic record
Abstract
BACKGROUND: In Canada, federal, provincial, and territorial governments are developing an ambitious project to implement an interoperable electronic health record (EHR). Benefits for patients, healthcare professionals, organizations, and the public in general are expected. However, adoption of an interoperable EHR remains an important issue because many previous EHR projects have failed due to the lack of integration into practices and organizations. Furthermore, perceptions of the EHR vary between end-user groups, adding to the complexity of implementing this technology. Our aim is to produce a comprehensive synthesis of actual knowledge on the barriers and facilitators influencing the adoption of an interoperable EHR among its various users and beneficiaries. METHODS: First, we will conduct a comprehensive review of the scientific literature and other published documentation on the barriers and facilitators to the implementation of the EHR. Standardized literature search and data extraction methods will be used. Studies' quality and relevance to inform decisions on EHR implementation will be assessed. For each group of EHR users identified, barriers and facilitators will be categorized and compiled using narrative synthesis and meta-analytical techniques. The principal factors identified for each group of EHR users will then be validated for its applicability to various Canadian contexts through a two-round Delphi study, involving representatives from each end-user groups. Continuous exchanges with decision makers and periodic knowledge transfer activities are planned to facilitate the dissemination and utilization of research results in policies regarding the implementation of EHR in the Canadian healthcare system. DISCUSSION: Given the imminence of an interoperable EHR in Canada, knowledge and evidence are urgently needed to prepare this major shift in our healthcare system and to oversee the factors that could affect its adoption and integration by all its potential users. This synthesis will be the first to systematically summarize the barriers and facilitators to EHR adoption perceived by different groups and to consider the local contexts in order to ensure the applicability of this knowledge to the particular realities of various Canadian jurisdictions. This comprehensive and rigorous strategy could be replicated in other settings.
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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 | 0.065 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".