A Comparison of How Canada, England, and Denmark are Managing their Electronic Health Record Journeys
Bibliographic record
Abstract
Healthcare is one of the world’s most information-intensive industries. Every day, volumes of data are produced which, properly used, can improve clinical practice and outcomes, guide planning and resource allocation, and enhance accountability. Electronic health information is fundamental to better healthcare. There will be no significant increase in healthcare quality and efficiency without high quality, user-friendly health information compiled and delivered electronically. The growing use of information and communication technology (ICT) in the healthcare sector has introduced numerous opportunities and benefits to patients, providers and governments alike. Patients are being provided with tools to help them manage and monitor their healthcare, providers are able to seamlessly access up-to-date patient information, and governments are showing transparency to the public by reporting health data and information on their websites. There is mounting evidence that national, regional, and organizational e-health strategies are being developed and implemented worldwide. This chapter provides an overview of three different national e-health strategies, and identifies the lessons learned from the e-health strategies of Canada, England and Denmark.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".