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


Journal

Pediatric quality & safety
ISSN: 2472-0054
Titre abrégé: Pediatr Qual Saf
Pays: United States
ID NLM: 101702480

Informations de publication

Date de publication:
Historique:
received: 11 02 2020
accepted: 16 10 2020
entrez: 15 3 2021
pubmed: 16 3 2021
medline: 16 3 2021
Statut: epublish

Résumé

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. 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. 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. 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.

Identifiants

pubmed: 33718751
doi: 10.1097/pq9.0000000000000396
pmc: PMC7952107
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e396

Informations de copyright

Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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Auteurs

Kenneth W McKinley (KW)

Emergency Medicine Section of Data Analytics, Children's National, Washington, D.C.

James M Chamberlain (JM)

Emergency Medicine Section of Data Analytics, Children's National, Washington, D.C.

Quynh Doan (Q)

Division of Emergency Medicine, Department of Pediatrics, British Columbia Children's Hospital, Vancouver, BC, Canada.

Deena Berkowitz (D)

Emergency Medicine Section of Data Analytics, Children's National, Washington, D.C.

Classifications MeSH