Impact of Universal Suicide Risk Screening in a Pediatric Emergency Department: A Discrete Event Simulation Approach.

OBJECTIVE: The aim of this study was to use discrete event simulation (DES) to model the impact of two universal suicide risk screening scenarios (emergency department [ED] and hospital-wide) on mean length of stay (LOS), wait times, and overflow of our secure patient care unit for patients being evaluated for a behavioral health complaint (BHC) in the ED of a large, academic children's hospital. METHODS: We developed a conceptual model of BHC patient flow through the ED, incorporating anticipated system changes with both universal suicide risk screening scenarios. Retrospective site-specific patient tracking data from 2017 were used to generate model parameters and validate model output metrics with a random 50/50 split for derivation and validation data. RESULTS: The model predicted small increases (less than 1 hour) in LOS and wait times for our BHC patients in both universal screening scenarios. However, the days per year in which the ED experienced secure unit overflow increased (existing system: 52.9 days; 95% CI, 51.5-54.3 days; ED: 94.4 days; 95% CI, 92.6-96.2 days; and hospital-wide: 276.9 days; 95% CI, 274.8-279.0 days). CONCLUSIONS: The DES model predicted that implementation of either universal suicide risk screening scenario would not severely impact LOS or wait times for BHC patients in our ED. However, universal screening would greatly stress our existing ED capacity to care for BHC patients in secure, dedicated patient areas by creating more overflow.

Validation of "Personal Protective Equipment Conservation Strategies Tool" to Predict Consumption of N95s, Facemasks, and Gowns During Pandemic-Related Shortages.

We sought to prospectively validate a model to predict the consumption of personal protective equipment in a pediatric emergency department during the COVID-19 pandemic. We developed the Personal Protective Equipment Conservation Strategies Tool, a Monte Carlo simulation model with input parameters defined by members of our emergency department personal protective equipment task force. Inputs include different conservation strategies that reflect dynamic reuse policies. Over the course of 4 consecutive weeks in April and May 2020, we used the model to predict the consumption of N95 respirators, facemasks, and gowns in our emergency department based on values for each input parameter. At the end of each week, we calculated the percent difference between actual consumption and predicted consumption based on model outputs. Actual consumption of personal protective equipment was within 20% of model predictions for each of the 4 consecutive weeks for N95s (range, -16.3% to 16.1%) and facemasks (range, -7.6% to 13.1%), using "maximum conservation" and "high conservation" strategies, respectively. Actual consumption of gowns was 11.8% less than predicted consumption for Week 1, gown resupply data were unavailable on Weeks 2-4. The Personal Protective Equipment Conservation Strategies Tool was prospectively validated for "maximum conservation" and "high conservation" models, with actual consumption within 20% of model predictions.