A novel emergency operating room online scheduling software: making the operating room more efficient and cost effective
Bibliographic record
Abstract
Operating room efficiency is invaluable for all medical systems across the globe but is especially important for public systems such as the one in Canada where resources are limited with many patients requiring care. A new online scheduling software ORNET.CA was created and installed in a level one trauma centre, the Montreal General Hospital (MGH), in Montreal, Canada. All nursing staff were then trained for its use. Physicians were also sent an email with instructions of its use (Appendix A,B). The pilot for the software was launched in October 2015. The results demonstrate that ORnet can improve OR efficiency by up to 10% by improving communication which represent an average cost saving of $267,325.99 annually in Quebec for a single hospital centre. We demonstrate that ORnet improves communication between hospital staff and physicians, reduces workflow interruption, and improves the quality of the working environment. In addition to saving money, the results showed that a simple scheduling software accessible to all health care staff allows for improvement in quality of life, and a leads to a decrease in stress and anxiety levels amongst residents. This in turn could potentially equate to a reduction in attrition rates among surgical residents. We also demonstrate that the software improves OR efficiency, potentially reducing personnel cost by approximately 10% annually
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".