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

Comput Inform Nurs

Author Affiliations: Department of Engineering Management and Systems Engineering, The George Washington University (Ms Rickard); and Emergency Medicine and Trauma Center (Drs Cohen and Ong, Mr Dwyer, and Ms Perritt) and Emergency Medicine Section of Data Analytics (Drs Chamberlain and McKinley), Children's National Hospital, Washington, DC; and Department of Emergency Medicine, University of California San Francisco (Dr Ong).

Published: January 2022

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745942PMC
http://dx.doi.org/10.1097/CIN.0000000000000831DOI Listing

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