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Using data envelopment analysis to perform benchmarking in intensive care units

BACKGROUND: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envel...

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Autores principales: Antunes, Bianca B. P., Bastos, Leonardo S. L., Hamacher, Silvio, Bozza, Fernando A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601512/
https://www.ncbi.nlm.nih.gov/pubmed/34793542
http://dx.doi.org/10.1371/journal.pone.0260025
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author Antunes, Bianca B. P.
Bastos, Leonardo S. L.
Hamacher, Silvio
Bozza, Fernando A.
author_facet Antunes, Bianca B. P.
Bastos, Leonardo S. L.
Hamacher, Silvio
Bozza, Fernando A.
author_sort Antunes, Bianca B. P.
collection PubMed
description BACKGROUND: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. METHODS: We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. RESULTS: We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79–1.21] and SRU was 1.15 [IQR: 0.95–1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18–1.88] vs. 1.7 [IQR: 1.36–2.00]) and nursing workload (168 hours [IQR: 168–291] vs 396 hours [IQR: 336–672]) but higher nurses per bed ratio (2.02 [1.16–2.48] vs. 1.71 [1.43–2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the “most efficient” quadrant. CONCLUSION: Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.
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spelling pubmed-86015122021-11-19 Using data envelopment analysis to perform benchmarking in intensive care units Antunes, Bianca B. P. Bastos, Leonardo S. L. Hamacher, Silvio Bozza, Fernando A. PLoS One Research Article BACKGROUND: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. METHODS: We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. RESULTS: We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79–1.21] and SRU was 1.15 [IQR: 0.95–1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18–1.88] vs. 1.7 [IQR: 1.36–2.00]) and nursing workload (168 hours [IQR: 168–291] vs 396 hours [IQR: 336–672]) but higher nurses per bed ratio (2.02 [1.16–2.48] vs. 1.71 [1.43–2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the “most efficient” quadrant. CONCLUSION: Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency. Public Library of Science 2021-11-18 /pmc/articles/PMC8601512/ /pubmed/34793542 http://dx.doi.org/10.1371/journal.pone.0260025 Text en © 2021 Antunes et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Antunes, Bianca B. P.
Bastos, Leonardo S. L.
Hamacher, Silvio
Bozza, Fernando A.
Using data envelopment analysis to perform benchmarking in intensive care units
title Using data envelopment analysis to perform benchmarking in intensive care units
title_full Using data envelopment analysis to perform benchmarking in intensive care units
title_fullStr Using data envelopment analysis to perform benchmarking in intensive care units
title_full_unstemmed Using data envelopment analysis to perform benchmarking in intensive care units
title_short Using data envelopment analysis to perform benchmarking in intensive care units
title_sort using data envelopment analysis to perform benchmarking in intensive care units
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601512/
https://www.ncbi.nlm.nih.gov/pubmed/34793542
http://dx.doi.org/10.1371/journal.pone.0260025
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