Factors associated with emergency department length of stay in Montreal - results of a quasi-experimental study and electronic medical record data analysis
Bibliographic record
Abstract
Implications and contribution:This thesis showed that it is possible and desirable to understand and evaluate organizational re-structuring / interventions, to explore their effect on efficiency of ED and predicting ED LOS based on covariates.In an inter-connected, dynamic system, we rarely have the opportunity to identify a purposeful systemic intervention to improve the ED efficiency.Health organizational interventions including site relocation results in some changes in the ED functioning and ED population dynamic, which allows for an evaluation of the effect of these changes on ED length of stay and ED efficiency.This thesis has shown that a systemic, purposeful intervention like site relocation and restructuring allows for focused study and discernment of variables contributing to improved ED efficiency, especially when coupled with modern machine learning techniques.Such techniques surpass conventional statistical modeling because they have the potential to provide greater insight for predicting ED LOS and for improving the efficiency of health services.These results may help in identifying factors that reduce ED LOS and may help in building an algorithm to potentially build a tool to predict ED waiting times based on relevant factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".