Multimedia Features in Electronic Health Records: An Analysis of Vendor Websites and Physicians' Perceptions
Bibliographic record
Abstract
Electronic health records (EHRs) facilitate storing, organizing, and sharing personal health information. The academic literature suggests that multimedia information (MM; image, audio, and video files) should be incorporated into EHRs. We examined the acceptability of MM-enabled EHRs for Ontario-based software vendors and physicians, using a qualitative analysis of primary and acute care EHR vendor websites, and a survey of physician perceptions regarding MM features in EHRs. Primary care EHR vendors provided more product-specific information than acute care vendors; however, neither group emphasized MM features in their EHRs. Physicians had slightly positive perceptions of image and video features, but not of audio features. None of the external factors studied predicted physicians‘ intention to use MM. Our findings suggest that neither vendors nor physicians are enthusiastic about implementing or using MM in EHRs, despite acknowledging potential benefits. Further research is needed to explore how to incorporate MM into EHR systems.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".