Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department
Bibliographic record
Abstract
Despite major investments in healthcare, access to front line health services (family doctors, walk-in clinics) is still difficult. If front line healthcare services remain insufficient, emergency departments will have to offer non-urgent patients appropriate services. More than half of emergency patients are ambulatory patients and their medical condition is not usually as serious as for patients on stretchers (Thibeault, 2014 Thibeault, J. (2014). Le «vrai» temps d’attente moyen aux urgences est de 4 heures, Ici Radio-Canada, Consulté le 11 février 2016, Tiré de: http://ici.radiocanada.ca/nouvelles/societe/2014/06/11/009-temps-attente-urgences-hopitaux-quebec-patients-ambulatoires-quatre-heures.shtml [Google Scholar]). This paper is devoted to the analysis of ambulatory patient length of stay in an emergency department of a hospital in the province of Quebec. The average length of stay for ambulatory patients in that hospital is slightly more than 7 hours which exceeds the average 4 hours observed for all emergency departments in Quebec. To identify the factors and their interactions affecting the performance of the hospital emergency, measured by the average length of stay for ambulatory patients, experimental design and discrete-event simulation were used. This research aims at verifying how nurses can contribute to reduce emergency department overcrowding. Our results show that giving more responsibility to nurses (collective prescriptions, review patients) reduce greatly the average patient length of stay with less financial effort than adding new doctors. This allows doctors to focus on the more acute patients.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".