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Related Experiment Video

Updated: Oct 13, 2025

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
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Using data envelopment analysis to perform benchmarking in intensive care units.

Bianca B P Antunes1, Leonardo S L Bastos1, Silvio Hamacher1

  • 1Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil.

Plos One
|November 18, 2021
PubMed
Summary
This summary is machine-generated.

Data envelopment analysis benchmarks Intensive Care Units (ICUs) by considering both performance and resources. Efficient ICUs utilize fewer physicians per bed but more nurses per bed, indicating optimized resource allocation for better patient care.

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Critical Care Medicine

Background:

  • Benchmarking Intensive Care Units (ICUs) using Data Envelopment Analysis (DEA) is under-researched.
  • Previous studies often overlooked resource allocation in efficiency comparisons.
  • This study addresses the need for a comprehensive benchmarking analysis of ICUs incorporating resource variables.

Purpose of the Study:

  • To conduct a benchmarking analysis of Intensive Care Units (ICUs) in Brazil using Data Envelopment Analysis (DEA).
  • To identify factors influencing ICU efficiency by analyzing staffing, structure, and strain variables.
  • To compare DEA results with the efficiency matrix method and provide actionable targets for non-efficient units.

Main Methods:

  • A retrospective analysis of observational data from the ORCHESTRA Study involving 93 ICUs and 129,680 patients.
  • Development of three DEA models using Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU) as outputs, and staffing, structure, and strain as inputs.
  • Comparison of efficient and non-efficient units, and validation against the efficiency matrix method.

Main Results:

  • Efficient ICUs demonstrated a lower physician-to-bed ratio and nursing workload, but a higher nurse-to-bed ratio compared to non-efficient units.
  • For-profit hospitals and specialized ICUs generally achieved better efficiency scores.
  • DEA results largely aligned with the efficiency matrix method, categorizing efficient units within the 'most efficient' quadrant.

Conclusions:

  • Data Envelopment Analysis (DEA) offers valuable insights for ICU managers, detailing both desired outcomes and necessary resource levels for efficient care.
  • The choice of input variables significantly influences the perspective on ICU efficiency.
  • Integrating DEA with the efficiency matrix method enhances the understanding of ICU performance and efficiency.