Validating the Emergency Department Avoidability Classification (EDAC): A cluster randomized single-blinded agreement study.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 08 11 2023
accepted: 10 01 2024
medline: 23 1 2024
pubmed: 23 1 2024
entrez: 23 1 2024
Statut: epublish

Résumé

The Emergency Department Avoidability Classification (EDAC) retrospectively classifies emergency department (ED) visits that could have been safely managed in subacute primary care settings, but has not been validated against a criterion standard. A validated EDAC could enable accurate and reliable quantification of avoidable ED visits. We compared agreement between the EDAC and ED physician judgements to specify avoidable ED visits. We conducted a cluster randomized, single-blinded agreement study in an academic hospital in Hamilton, Canada. ED visits between January 1, 2019, and December 31, 2019 were clustered based on EDAC classes and randomly sampled evenly. A total of 160 ED visit charts were randomly assigned to ten participating ED physicians at the academic hospital for evaluation. Physicians judged if the ED visit could have been managed appropriately in subacute primary care (an avoidable visit); each ED visit was evaluated by two physicians independently. We measured interrater agreement between physicians with a Cohen's kappa and 95% confidence intervals (CI). We evaluated the correlation between the EDAC and physician judgements using a Spearman rank correlation and ordinal logistic regression with odds ratios (ORs) and 95% CIs. We examined the EDAC's precision to identify avoidable ED visits using accuracy, sensitivity and specificity. ED physicians agreed on 139 visits (86.9%) with a kappa of 0.69 (95% CI 0.59-0.79), indicating substantial agreement. Physicians judged 96.2% of ED visits classified as avoidable by the EDAC as suitable for management in subacute primary care. We found a high correlation between the EDAC and physician judgements (0.64), as well as a very strong association to classify avoidable ED visits (OR 80.0, 95% CI 17.1-374.9). The EDACs avoidable and potentially avoidable classes demonstrated strong accuracy to identify ED visits suitable for management in subacute care (82.8%, 95% CI 78.2-86.8). The EDAC demonstrated strong evidence of criterion validity to classify avoidable ED visits. This classification has important potential for accurately monitoring trends in avoidable ED utilization, measuring proportions of ED volume attributed to avoidable visits and informing interventions intended at reducing ED use by patients who do not require emergency or life-saving healthcare.

Identifiants

pubmed: 38261589
doi: 10.1371/journal.pone.0297689
pii: PONE-D-23-36307
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0297689

Informations de copyright

Copyright: © 2024 Strum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Ryan P Strum (RP)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.

Shawn Mondoux (S)

Department of Medicine, Division of Emergency Medicine, McMaster University, Hamilton, Ontario, Canada.
Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Fabrice I Mowbray (FI)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
College of Nursing, Michigan State University, East Lansing, Michigan, United States of America.

Lauren E Griffith (LE)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
McMaster Institute for Research and Aging, McMaster University, Hamilton, Ontario, Canada.

Andrew Worster (A)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Department of Medicine, Division of Emergency Medicine, McMaster University, Hamilton, Ontario, Canada.

Walter Tavares (W)

The Wilson Centre, University of Toronto, Toronto, Ontario, Canada.

Paul Miller (P)

Department of Medicine, Division of Emergency Medicine, McMaster University, Hamilton, Ontario, Canada.
Centre for Paramedic Education and Research, Hamilton Health Sciences, Hamilton, Ontario, Canada.

Komal Aryal (K)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.

Ravi Sivakumaran (R)

Health Information Management Department, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada.

Andrew P Costa (AP)

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.

Classifications MeSH