Automating Physician Scheduling in Family Medicine Groups: Achieving Equity and Workload Balance Through a Hierarchical Approach
Bibliographic record
Abstract
This paper explores the automation of physician scheduling in family medicine groups. Our problem is derived from the particular case of our partner: a Family Medicine Group (FMG) located in Quebec city (Clinique Maizerets GMF universitaire) who wishes to change their current schedule creation process from manual to automated and optimized scheduling. We aim to develop a tool to rapidly create “good” schedules respecting all the required constraints while optimizing two main objectives: (i) equity in tasks assignments between physicians and (ii) balance in weekly and daily workloads over the planning horizon. A hierarchical approach is proposed in which a series of decisional problems are sequentially solved. Each problem is formulated as a Mixed Integer Programming (MIP) model and solved with either an open optimization solver (CoinBC) or a commercial solver (CPLEX). The performance of both solvers are compared in terms of solution quality and computing times. A number of key performance indicators are proposed to evaluate the quality of the resulting schedules. Some preliminary results obtained by applying our approach to a problem test provided by our partner are presented and discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".