Building an Electronic Handover Tool for Physicians Using a Collaborative Approach between Clinicians and the Development Team
Bibliographic record
Abstract
In an effort by The Ottawa Hospital (TOH) to become one of the top 10% performers in patient safety and quality of care, the hospital embarked on improving the communication process during handover between physicians by building an electronic handover tool. It is expected that this tool will decrease information loss during handover. The Information Systems (IS) department engaged a workgroup of physicians to become involved in defining requirements to build an electronic handover tool that suited their clinical handover needs. This group became ultimately responsible for defining the graphical user interface (GUI) and all functionality related to the tool. Prior to the pilot, the Information Systems team will run a usability testing session to ensure the application is user friendly and has met the goals and objectives of the workgroup. As a result, The Ottawa Hospital has developed a fully integrated electronic handover tool built on the Clinical Mobile Application (CMA) which allows clinicians to enter patient problems, notes and tasks available to all physicians to facilitate the handover process.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".