Developing a Strategy for the Improvement in Patient Experience in a Canadian Academic Department of Surgery
Bibliographic record
Abstract
Patient experience (PE) is recognized as a key component in the quality of health-care delivery. Public reporting of hospital, division, and physician-specific PE results has added to the momentum of adopting strategies to augment this metric of care. The Ottawa Hospital embarked on a journey to improve PE as a pillar of its quality improvement plan. This article demonstrates the efforts of a single surgery department from one large urban center to improve in-hospital PE in the rapidly changing environment of medicine and surgery. A multidisciplinary group within the department and a focus group of previous surgical inpatients were organized to address immediate challenges related to inpatient PE issues. We identified concrete strategies to optimize pain control, perceptions of patient respect and dignity, perceptions of surgeon availability, discharge medication understanding, and overall experience. Also, we identified a need in our department for timely patient feedback, improved communication styles in our staff and trainees, and an internal curriculum offering additional training for our staff and residents. We anticipate that the current results would be of significant interest to other departments wishing to optimize their PE profile as part of the ongoing quality improvement process at hospitals across North America.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".