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Reducing Pediatric ED Length of Stay by Reducing Diagnostic Testing: A Discrete Event Simulation Model
Kenneth W McKinley1, James M Chamberlain1, Quynh Doan2
1Emergency Medicine Section of Data Analytics, Children's National, Washington, D.C.
Discrete event simulation (DES) modeling predicted shorter mean emergency department (ED) length of stay (LOS) for pediatric patients with reduced diagnostic testing. This quality improvement strategy shows promise for evaluating healthcare initiatives.
Area of Science:
- Healthcare operations research
- Pediatric emergency medicine
- Quality improvement science
Background:
- Quality improvement initiatives require substantial investment before their impact is clear.
- Evaluating the system-level effects of changes in healthcare delivery is complex.
- Discrete event simulation (DES) offers a method to model and predict outcomes.
Purpose of the Study:
- To test the theoretical impact of reducing diagnostic testing for low-acuity pediatric emergency department (ED) patients using DES modeling.
- To forecast how changes in diagnostic testing rates affect ED length of stay (LOS).
- To evaluate the potential effectiveness of a quality improvement initiative before implementation.
Main Methods:
- An existing DES model of a pediatric ED was modified to include local diagnostic testing rates for Emergency Severity Index (ESI) 4 and 5 patients.
- The model was validated by comparing its output predictions to actual site-specific data for mean LOS and wait times.
- The Achievable Benchmark of Care methodology was used to determine a goal reduction in diagnostic testing rates.
Main Results:
- The validated DES model accurately approximated actual clinical data, showing no statistically significant differences.
- Setting diagnostic testing rates at an achievable benchmark significantly reduced the mean LOS for ESI 4 patients by 19.1 minutes compared to current rates.
- The model demonstrated a significant decrease in mean LOS for ESI 4 pediatric ED patients.
Conclusions:
- DES modeling predicted a statistically significant decrease in mean LOS for ESI 4 pediatric ED patients when diagnostic testing aligns with achievable benchmark rates.
- DES is a valuable tool for predicting the impact of quality improvement initiatives in healthcare settings prior to their implementation.
- This study supports the use of simulation modeling for healthcare quality improvement planning.
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