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. 2021 Mar 10;6(2):e396.
doi: 10.1097/pq9.0000000000000396. eCollection 2021 Mar-Apr.

Reducing Pediatric ED Length of Stay by Reducing Diagnostic Testing: A Discrete Event Simulation Model

Kenneth W McKinley  1 , James M Chamberlain  1 , Quynh Doan  2 , Deena Berkowitz  1
Affiliations

Reducing Pediatric ED Length of Stay by Reducing Diagnostic Testing: A Discrete Event Simulation Model

Kenneth W McKinley et al. Pediatr Qual Saf. .

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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Figures

Fig. 1.
Fig. 1.
Low-acuity visits from July 1, 2017, to June 30, 2018 were included in the modified model’s development.
Fig. 2.
Fig. 2.
Biannual provider feedback: Percentage of patient visits with diagnostic testing by provider. July 1–December 31, 2019.
Fig. 3.
Fig. 3.
Conceptual model of patient flow.
See this image and copyright information in PMC

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