Comparing Approaches to Measuring the Adoption and Usability of Electronic Health Records: Lessons Learned from Canada, Denmark and Finland
Bibliographic record
Abstract
Internationally, the adoption of health information technology is increasing. However, a number of issues have complicated the adoption of electronic health records (EHRs). In addition to adoption issues, it is becoming increasingly recognized that healthcare providers face a variety of usability issues. In this paper, we consider approaches that have been taken to assess both adoption and usability of EHRs in Canada, Denmark and Finland. Although all three countries deploy surveys to assess adoption, the approach and focus of the surveys differs across the countries. In Denmark and Finland, these surveys are dedicated to assessing information technology (IT) usage; while in Canada, questions about IT usage are part of a larger physician survey. Regarding usability, approaches vary considerably. In Finland, the approach includes a national survey about EHR usability. In Canada, ratings of system usability are reported regionally on web sites; while in Denmark, regional study results are reported based on evaluation of commercial products. This paper highlights the need to consider different evaluation approaches internationally.
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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 | 0.083 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".