Reducing surgical ward congestion at the vancouver island health authority through improved surgical scheduling
Bibliographic record
Abstract
As a consequence of high surgical bed occupancy levels in Vancouver Island Health Authority (VIHA) hospitals, staff stress levels, surgical cancellations and wait times for surgeries were becoming problematic. In collaboration with VIHA management and through site visits, interviews, and data analyses, we found that this congestion was in part attributable to current surgical scheduling practices which focussed on efficient use of the operating rooms but ignored the downstream bed utilization patterns caused by these schedules. We developed two tools, the Bed Utilization Simulator (BUS) and the Surgical Scheduling Optimizer (SSO) to improve current scheduling practices. BUS is a Monte Carlo simulation model written in Visual Basic for Applications in MS Excel that predicts inpatient bed utilization patterns for a specified surgical schedule entered through a graphical interface. This highly portable model imports historical patient records from hospital information systems to accurately represent historical patient mix when assessing schedules. SSO is a mixed integer programming model developed to provide schedules, as well as scheduling principles, that achieve minimal day to day variation in ward occupancy. Surgical planners could then use BUS to assess and revise these schedules, to account for factors not captured in SSO. These tools have been used on a “What if? ” basis to
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".