Developing a Pathologists’ Monthly Assignment Schedule: A Case Study at the Department of Pathology and Laboratory Medicine of The Ottawa Hospital
Bibliographic record
Abstract
In the Department of Pathology and Laboratory Medicine, at the beginning of each month, the clinical managers use expert knowledge to assign pathologists to expected daily specimens based on the criteria of workload restrictions, clinical sub-specialties, and availability. Since the size of the pathologists’ assignment problem is large, finding a feasible assignment manually is a very time-consuming process that takes a number of iterations over a number of days to complete. Moreover, every time there is a need to make a revision, a new assignment needs to be developed taking into account all the above criteria. The goal of this research is to develop an optimization model and a decision support tool that will help with monthly staffing of pathologists based on the criteria outlined above. The developed model is rooted in the classical operations research assignment problem and it is extended to account for the following requirements: each pathologist should be assigned to a similar specimen type throughout a week; for a given pathologist, there should be a rotation of the specimen types between the weeks; and the clinical managers’ preferences in terms of assigning a particular specimen type to a particular pathologist on a specific day need to be considered. A monthly assignment model covering 36 pathologists and 26 specimen types was solved using IBM ILOG CPLEX Optimization Studio. It is embedded in a decision support tool that helps clinical managers to make staffing decisions. The decision support tool has been validated using data from The Ottawa Hospital (TOH).
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".