Can an Emergency Surgery Scheduling Software Improve Residents’ Time Management and Quality of Life?
Bibliographic record
Abstract
Background: Operating room efficiency is invaluable. Particularly in public health systems, where resources are limited and patient loads are high, efficient systems underpin the continued delivery of high quality care. In addition to impacting patients, the implementation of efficient healthcare tools has the potential to improve staff quality of life. In the face of growing surgical resident attrition and healthcare worker burnout, developments in standard practice, such as the implementation of the 80-hour work week, are necessary to improve quality of life. Materials and methods: A new online scheduling software (ORNET.CA) was created, installed, and piloted in a Level I Trauma Center after instructing users (physicians and nurses) on its use. A 20-item survey was then distributed to all users to assess the effect implementation of the software had on their quality of life. Results: ORnet was shown to improve communication between hospital staff and physicians, reduce workflow interruptions, and improve the quality of the working environment. The survey showed that 60% of residents and 50% of attending staff believed that ORNET.CA improved their quality of life. Conclusions: We present data from a novel emergency operating room scheduling system that allowed surgical residents and attending physicians to better plan their on-call shifts. Staff (resident and physician) reported survey results suggest that implementation of this system resulted in an improved quality of life and a decrease in stress and anxiety levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".