Studying nurse workload and patient waiting time in a hematology-oncology clinic with discrete event simulation
Bibliographic record
Abstract
Our study is performed in a hematology-oncology clinic in Québec. This clinic experienced a 20% increase for hematology treatments and a 131% increase for oncology treatments. Clinic managers and personnel felt that this increase led to higher patient waiting time and personnel workload. Clinic managers decided to examine the possibility of adding resources to alleviate nurse workload. Patient trajectories and lead times, appointment scheduling and nurse workload are analyzed with a discrete-event simulation model. It is shown that patient waiting time is not too long. A nurse overload problem is observed with a nurse occupancy rate of 86.98% in the morning and 64.48% in the afternoon. New schedule appointments taking into account nurse capacity are proposed. These result in a decrease of the difference in nurse occupancy rates in the morning and in the afternoon.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".