Emergency Physicians' Perceptions of Health Information Exchange
Bibliographic record
Abstract
BACKGROUND: Health information exchange (HIE) is a potentially powerful technology that can improve the quality of care delivered in emergency departments, but little is known about emergency physicians' current perceptions of HIE. OBJECTIVES: This study sought to assess emergency physicians' perceived needs and knowledge of HIE. METHODS: A questionnaire was developed based on heuristics from the literature and implemented in a Web-based tool. The survey was sent as a hyperlink via e-mail to 371 attending emergency physicians at 12 hospitals in New York City. RESULTS: The response rate was 58% (n = 216). Although 63% said more than one quarter of their patients would benefit from external health information, the barriers to obtain it without HIE are too high--85% said it was difficult or very difficult to obtain external data, taking an average of 66 minutes, 72% said that their attempts fail half of the time, and 56% currently attempt to obtain external data less than 10% of the time. When asked to create a rank-order list, electrocardiograms (ECGs) were ranked the highest, followed by discharge summaries. Respondents also chose images over written reports for ECGs and X-rays, but preferred written reports for advanced imaging and cardiac studies. CONCLUSION: There is a strong perceived need for HIE, most respondents were not aware of HIE prior to this study, and there are certain types of data and presentations of data that are preferred by emergency physicians in the New York City region.
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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 | 0.009 | 0.054 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".