Physicians’ experience adopting the electronic transfer of care communication tool: barriers and opportunities
Bibliographic record
Abstract
Chloe de Grood, Katherine Eso, Maria Jose Santana Department of Community Health Sciences, W21C Research and Innovation Centre, Institute of Public Health, University of Calgary, Calgary, AB, Canada Purpose: The purpose of this study was to assess physicians' perceptions on a newly developed electronic transfer of care (e-TOC) communication tool and identify barriers and opportunities toward its adoption. Participants and methods: The study was conducted in a tertiary care teaching center as part of a randomized controlled trial assessing the efficacy of an e-TOC communication tool. The e-TOC technology was developed through iterative consultation with stakeholders. This e-TOC summary was populated by acute care physicians (AcPs) and communicated electronically to community care physicians (CcPs). The AcPs consisted of attending physicians, resident trainees, and medical students rotating through the Medical Teaching Unit. The CcPs were health care providers caring for patients discharged from hospital to the community. AcPs and CcPs completed validated surveys assessing their experience with the newly developed e-TOC tool. Free text questions were added to gather general comments from both groups of physicians. Units of analysis were individual physicians. Data from the surveys were analyzed using mixed methods. Results: AcPs completed 138 linked pre- and post-rotation surveys. At post-rotation, each AcP completed an average of six e-TOC summaries, taking an average of 37 minutes per e-TOC summary. Over 100 CcPs assessed the quality of the TOC summaries, with an overall rating of 8.3 (standard deviation: 1.48; on a scale of 1–10). Thematic analyses revealed barriers and opportunities encountered by physicians toward the adoption of the e-TOC tool. While the AcPs highlighted issues with timeliness, usability, and presentation, the CcPs identified barriers accessing the web-based TOC summaries, emphasizing that the summaries were timely and the quality of information supported continuity of care. Conclusion: Despite the barriers identified by both groups of physicians, the e-TOC communication tool was well received. Our experience can serve as a template for other health research teams considering the implementation of e-health technologies into health care systems. Keywords: e-health, transition, summary, informatics
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".