Bottleneck detection for improvement of Emergency Department efficiency
Bibliographic record
Abstract
Purpose – The purpose of this paper is to introduce a method of the bottleneck detection for Emergency Department (ED) improvement using benchmarking and design of experiments (DOE) in simulation model. Design/methodology/approach – Four procedures of treatments are used to represent ED activities of the patient flow. Simulation modeling is applied as a cost-effective tool to analyze the ED operation. Benchmarking provides the achievable goal for the improvement. DOE speeds up the process of bottleneck search. Findings – It is identified that the long waiting time is accumulated by previous arrival patients waiting for treatment in the ED. Comparing the processing time of each treatment procedure with the benchmark reveals that increasing the treatment time mainly happens in treatment in progress and emergency room holding (ERH) procedures. It also indicates that the to be admitted time caused by the transfer delay is a common case. Research limitations/implications – The current research is conducted in the ED only. Activities in the ERH require a close cooperation of several medical teams to complete patients’ condition evaluations. The current model may be extended to the related medical units to improve the model detail. Practical implications – ED overcrowding is an increasingly significant public healthcare problem. Bottlenecks that affect ED overcrowding have to be detected to improve the patient flow. Originality/value – Integration of benchmarking and DOE in simulation modeling proposed in this research shows the promise in time-saving for bottleneck detection of ED operations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".