Barriers to Successful Health Information Exchange Systems in Canada and the USA
Bibliographic record
Abstract
Background: Despite the potential benefits of health information exchange (HIE) and the two decades of efforts from the Canadian and the American governments to promote health exchange projects, failures far outnumber successes. Objective: To better understand the barriers influencing the adoption and implementation of inter-organization HIE systems in Canada and the USA. Method: A systematic literature review was conducted to examine English-language studies that identified barriers to HIE in Canada and the USA between 1995 and 2016. Electronic databases, backward searching and expert consultations were used. Results: 31 articles have been included. There is a dearth of publications reported on the HIE barriers in Canada. A total of 33 barriers have been identified. Conclusion: There are noticeable differences in the barriers reported in these countries. Privacy concerns and a lack of stakeholder buy-in are recurring barriers over time in the USA. Low adoption of electronic medical records is the main barrier in Canada.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".