A snapshot of health information exchange across five nations: an investigation of frontline clinician experiences in emergency care
Bibliographic record
Abstract
Objective: Ensuring the ability to exchange patient information among disparate electronic health records systems is a top priority and a domain of substantial public investment across countries. However, we know little about the extent to which current capabilities meet the needs of frontline clinicians. Materials and Methods: We conducted in-person, semistructured interviews with emergency care physicians and nurses in select hospitals in Canada, Denmark, Finland, Germany, and the USA. We characterized the state of health information exchange (HIE) by country and used thematic analysis to identify the perceived benefits of access to complete past medical history (PMH), the conditions under which PMH is sought, and the challenges to accessing and using HIE capabilities. Results: HIE approaches, and the information electronically accessible to clinicians, differed by country. Benefits of access to PMH included safer care, reduced patient length of stay, and fewer lab and imaging orders. Conditions under which PMH was sought included moderate-acuity patients, patients with chronic conditions, and instances where accessing PMH was convenient. Challenges to HIE access and use included difficulty knowing where information is located, delay in receiving information, and difficulty finding information within documents. Discussion: Even with different HIE approaches across countries, all clinicians reported shortcomings in their country's approach. Notably, challenges were similar and shaped the conditions under which PMH was sought. Conclusion: As countries continue to pursue broad-based HIE, they appear to be facing similar challenges in realizing HIE value and therefore have an opportunity to learn from one another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".