Discrete event simulation modelling to evaluate the impact of a quality improvement initiative on patient flow in a paediatric emergency department
Bibliographic record
Abstract
OBJECTIVE: We developed a discrete event simulation model to evaluate the impact on system flow of a quality improvement (QI) initiative that included a time-specific protocol to decrease the time to antibiotic delivery for children with cancer and central venous catheters who present to a paediatric ED with fever. METHODS: The model was based on prospective observations and retrospective review of ED processes during the maintenance phase of the QI initiative between January 2016 and June 2017 in a large, urban, academic children's hospital in New York City, USA. We compared waiting time for full evaluation (WT) and length of stay (LOS) between a model with and a model without the protocol. We then gradually increased the proportion of patients receiving the protocol in the model and recorded changes in WT and LOS. RESULTS: We validated model outputs against administrative data from 2016, with no statistically significant differences in average WT or LOS for any emergency severity index (ESI). There were no statistically significant differences in these flow metrics between the model with and the model without the protocol. By increasing the proportion of total patients receiving this protocol, from 0.2% to 1.3%, the WT increased by 2.8 min (95% CI: 0.6 to 5.0) and 7.6 min (95% CI: 2.0 to 13.2) for ESI 2 and ESI 3 patients, respectively. This represents a 14.0% increase in WT for ESI 3 patients. CONCLUSIONS: Simulation modelling facilitated the testing of system effects for a time-specific protocol implemented in a large, urban, academic paediatric ED, showing no significant impact on patient flow. The model suggests system resilience, demonstrating no detrimental effect on WT until there is a 7-fold increase in the proportion of patients receiving the protocol.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| 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".