Multivariate performance indices in Federal University Hospitals : an exploratory analysis using principal component analysis - Peixoto, Maria Gabriela Mendonça (2026)
Purpose - This study aims to measure and explain the performance of Brazilian Federal University Hospitals (HUFs) using an integrated multivariate approach that combines Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). The research seeks to identify the main factors influencing efficiency across teaching, research, financial, and care management dimensions. By constructing interpretable composite indices, the study supports evidence-based decision-making in public healthcare, contributing to the formulation of managerial strategies and public policies that enhance hospital effectiveness, optimize resource allocation, and promote sustainable improvements within Brazil's Unified Health System (SUS). Design/methodology/approach A descriptive and quantitative research design was adopted. Data were collected from the SIMEC/REHUF database, encompassing 33 performance indicators from four managerial dimensions: Teaching and Research, Care Management, Economic-Financial Management, and Infrastructure and Management. PCA was applied to reduce dimensionality and construct multivariate indices summarizing hospital performance, while DEA identified efficient and inefficient units. The integrated PCA–DEA approach provided a comprehensive framework for assessing and benchmarking HUF efficiency, allowing the interpretation of hospital performance based on objective, replicable, and policy-relevant indicators. Findings Three principal components (PCs) explained 64.10% of total variance and characterized hospital performance. The first represented investment and expenditure efficiency in material and human resources; the second reflected teaching and research performance; and the third captured investment in human resources and practical training under critical conditions. The combined PCA–DEA model enabled the ranking of hospitals and the identification of efficiency patterns among decision-making units (DMUs). The results validated the integrated approach as effective for diagnosing performance differences and supporting the development of improvement strategies in Brazil's federal university hospital network. Research limitations/implications The study's main limitation concerns the use of 2014 data from SIMEC/REHUF due to incomplete records in later years. Consequently, the analysis may not capture recent structural or managerial reforms in the HUF network. Additionally, missing data treatment and the exclusion of certain indicators may have introduced bias. Future studies should incorporate updated datasets, longitudinal analyses, and additional performance dimensions such as patient satisfaction and technological innovation. Despite these limitations, the integrated PCA–DEA framework offers a robust methodological basis for continued research on hospital efficiency and multivariate performance assessment. Practical implications The study provides hospital managers and policymakers with actionable tools for performance evaluation. The three multivariate indices can guide the prioritization of investments, resource distribution, and quality-improvement programs within the HUF system. By identifying efficient and inefficient hospitals, the framework allows benchmarking and supports the development of performance-based funding models. The findings also facilitate continuous monitoring of managerial indicators and the assessment of policy outcomes, thus strengthening evidence-based decision-making in Brazil's public health system and enhancing accountability and transparency in the use of public resources. Social implications The research contributes to improving the quality, accessibility, and efficiency of public healthcare services in Brazil. By revealing performance disparities among Federal University Hospitals, the model supports equity in health resource allocation and reinforces the social role of these institutions in education, research, and patient care. Enhanced managerial capacity and optimized performance directly benefit the population served by the Unified Health System (SUS), particularly vulnerable groups dependent on university hospitals. The study also fosters public accountability and supports sustainable health policy development grounded in quantitative evidence. Originality/value This study is among the first to integrate PCA and DEA to evaluate the performance of Brazil's Federal University Hospitals. It provides an innovative methodological framework capable of handling multidimensional data and generating interpretable efficiency indices. The research extends previous applications of these techniques by adapting them to the public healthcare context and using official institutional data. Its originality lies in combining descriptive and prescriptive analytics to support management and policy formulation, offering both theoretical advancement and practical contributions to hospital performance assessment.