Anesthesia Practice Shift Scheduling With a Generative Deep Learning Model
Bibliographic record
Abstract
Anesthesiology scheduling techniques are inadequate to appropriately deal with modern anesthesia practice demands. Anesthesiologists are increasingly dissatisfied with their jobs in the face of inflexible schedules, increasing workload, and the complexity of practice. Decreasing autonomy and inherent responsibility lead to burnout and decreased job satisfaction. Unfortunately, equitable and timely shift scheduling that meets individual provider expectations remains a distant mirage. Technology has been promised as a means to decrease workload and improve productivity. But technology has not met these expectations. In real-world anesthesia practice, scheduling remains contentious and time-consuming. These failures are somewhat attributable to current scheduling systems and software. In this paper, we present an alternative method of anesthesiology shift scheduling using advances in machine learning (ML). The development of this deep learning (DL) model for shift scheduling drastically reduces the effort required to create shift schedules that comply with the rules and regulations observed by anesthesia practices. A DL model architecture is developed, trained with shift schedule data from the Reno-Tahoe Anesthesia (RTA) group, and evaluated against the practice requirements. The DL model trained and evaluated demonstrates a Matthews Correlation Coefficient (MCC) of 0.9776 and balanced accuracy of 0.9531. The trained model reliably learns practice scheduling rules sufficient to generate new shift schedules in compliance with the rules. Furthermore, the trained model learns practice rules solely from past examples without requiring a human expert to codify the rules.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".