Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness
Bibliographic record
Abstract
Abstract Purpose Excessive resident duty hours (RDH) is a recognized issue with implications for physician well-being and patient safety. A significant component of the RDH concern is on-call duty. While other industries have adopted machine learning models (MLMs) to optimize scheduling and employee well-being, medicine has lagged. This study aimed to investigate the use of MLMs to predict demand on orthopaedic residents to optimize scheduling. Methods Daily surgical handover emails over an eight-year (2012–2019) period at a level I trauma centre were used to model demand on residents. Various MLMs were trained to predict the workload, with their results compared to the current approach. Quality of models was determined by using the area under the receiver operator curve (AUC) and accuracy. The top ten most important variables were extracted from the most successful model. Results The reduction in orthopaedic resident shifts possible per annum was 24.7%. The most successful model during testing was the neural network (AUC: 0.81, accuracy: 73.7%). All models were better than the current approach (AUC: 0.50, accuracy: 50.1%). Key variables used by the neural network model were (descending order): spine call duty (y/n), year, weekday/weekend, month, and day of the week. Conclusion This was the first study using MLMs to predict demand for orthopaedic residents at a major academic institution. All MLMs were more successful than the current scheduling approach. Future work should look to incorporate predictive models with optimization strategies, matching scheduling with demand to improve resident well-being and patient care. Level of evidence: Level III.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".