Reducing Pediatric ED Length of Stay by Reducing Diagnostic Testing: A Discrete Event Simulation Model
Bibliographic record
Abstract
Quality improvement efforts can require significant investment before the system impact of those efforts can be evaluated. We used discrete event simulation (DES) modeling to test the theoretical impact of a proposed initiative to reduce diagnostic testing for low-acuity pediatric emergency department (ED) patients. METHODS: We modified an existing DES model, built at another large, urban, academic pediatric ED, to forecast the impact of reducing diagnostic testing rates on mean ED length of stay (LOS). The modified model included local testing rates for Emergency Severity Index (ESI) 4 and 5 patients and additional processes defined by local experts. Validation was performed by comparing model output predictions of mean LOS and wait times to actual site-specific data. We determined the goal reduction in diagnostic testing rates using the Achievable Benchmark of Care methodology. Model output mean LOS and wait times, with testing set at benchmark rates, were compared to outputs with testing set at current levels. RESULTS: During validation testing, model output metrics approximated actual clinical data with no statistically significant differences. Compared to model outputs with current testing rates, the mean LOS with testing set at an achievable benchmark was significantly shorter for ESI 4 (difference 19.1 mins [95% confidence interval 12.2, 26.0]) patients. CONCLUSION: A DES model predicted a statistically significant decrease in mean LOS for ESI 4 pediatric ED patients if diagnostic testing is performed at an achievable benchmark rate compared to current rates. DES shows promise as a tool to evaluate the impact of a QI initiative before implementation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".