Development of a Novel Web-Based Tool to Improve Emergency Department Communication with General Practitioners: A Needs Assessment Survey
Bibliographic record
Abstract
Background: Communication of emergency department (ED) visit information to general practitioners (GP) is often inadequate and can negatively impact on patient care.Further, the use of email as a communication tool between GP and ED providers has been not been well explored.Objectives: We sought to assess the desirability, feasibility and ideal functionalities of a novel web-based, automated post-ED visit communication tool for GPs.Methods: A cross-sectional needs assessment survey was conducted among the top 300 referring GPs to a single ED in Toronto, Canada.The main outcome measures were: current GP awareness of patient ED visits and anticipated uptake of an electronic notification and health record communication tool.Results: One hundred ninety-eight physicians responded (66% response rate).Fifty-eight percent of GPs were unaware or only sometimes aware of patients' ED visits.Nearly all (94%) would welcome an automated electronic system to communicate post-ED discharge health information in real-time.Two-thirds (67%) were in favour of their patients having online access to their own health records.Physicians less than 50 years of age were more likely than those greater than 50 to use both an office computer with internet and email access (96% versus 69%)and an electronic medical record (EMR;57% versus 41%). Conclusions:This needs assessment survey highlights an unmet need for improved ED-GP health record communication and suggests that GP uptake of a novel web-based post-ED visit notification and health record transfer system would be high.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".