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Using data envelopment analysis to perform benchmarking in intensive care units

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  • Bianca B P Antunes
  • Leonardo S L Bastos
  • Silvio Hamacher
  • Fernando A Bozza

Abstract

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.

Suggested Citation

  • Bianca B P Antunes & Leonardo S L Bastos & Silvio Hamacher & Fernando A Bozza, 2021. "Using data envelopment analysis to perform benchmarking in intensive care units," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-13, November.
    • Handle: RePEc:plo:pone00:0260025
    DOI: 10.1371/journal.pone.0260025
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    5. Diogo Ferreira & Rui Cunha Marques, 2018. "Identifying congestion levels, sources and determinants on intensive care units: the Portuguese case," Health Care Management Science, Springer, vol. 21(3), pages 348-375, September.
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