Using data envelopment analysis to perform benchmarking in intensive care units

<h4>Background</h4> 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 I...

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Autores principales:	Bianca B. P. Antunes, Leonardo S. L. Bastos, Silvio Hamacher, Fernando A. Bozza
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2c34f6a8ca646cb83052bd2bcd0c6c0
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spelling	oai:doaj.org-article:e2c34f6a8ca646cb83052bd2bcd0c6c02021-11-25T06:19:39ZUsing data envelopment analysis to perform benchmarking in intensive care units1932-6203https://doaj.org/article/e2c34f6a8ca646cb83052bd2bcd0c6c02021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601512/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Results</h4> 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. <h4>Conclusion</h4> 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.Bianca B. P. AntunesLeonardo S. L. BastosSilvio HamacherFernando A. BozzaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bianca B. P. Antunes Leonardo S. L. Bastos Silvio Hamacher Fernando A. Bozza Using data envelopment analysis to perform benchmarking in intensive care units
description	<h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Results</h4> 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. <h4>Conclusion</h4> 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.
format	article
author	Bianca B. P. Antunes Leonardo S. L. Bastos Silvio Hamacher Fernando A. Bozza
author_facet	Bianca B. P. Antunes Leonardo S. L. Bastos Silvio Hamacher Fernando A. Bozza
author_sort	Bianca B. P. Antunes
title	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_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_sort	using data envelopment analysis to perform benchmarking in intensive care units
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e2c34f6a8ca646cb83052bd2bcd0c6c0
work_keys_str_mv	AT biancabpantunes usingdataenvelopmentanalysistoperformbenchmarkinginintensivecareunits AT leonardoslbastos usingdataenvelopmentanalysistoperformbenchmarkinginintensivecareunits AT silviohamacher usingdataenvelopmentanalysistoperformbenchmarkinginintensivecareunits AT fernandoabozza usingdataenvelopmentanalysistoperformbenchmarkinginintensivecareunits
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Using data envelopment analysis to perform benchmarking in intensive care units

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