ASSESSING THE EFFICIENCY OF HOSPITAL DEPARTMENT PERFORMANCE PERIODS USING THE MALMQUIST PRODUCTIVITY INDEX

Abstract

This paper presents a quantitative assessment of operational efficiency across hospital departments at Zadar General Hospital, Croatia, over the 2022-2024 period, employing Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) methodologies. The research evaluates the relative efficiency of 11 departments by analysing the relationship between input variables (number of beds and physicians) and output variables (number of discharged patients and outpatient examinations), providing insights into productivity dynamics within the hospital system.
The study utilizes an input-oriented CCR model to establish efficiency scores and applies the Malmquist index to measure productivity changes over time. This methodological approach enables both static efficiency evaluation and dynamic tracking of performance trends, with productivity decomposed into technical efficiency change and technological change components. The longitudinal design overcomes limitations inherent in cross-sectional studies by capturing efficiency fluctuations across multiple time periods. Results reveal significant heterogeneity among departments, with substantial variations in resource distribution and operational capacity. The average number of physicians was 36 (SD=36.39), ranging from 9 to 128, while the average number of beds was 18 (SD=13.66), ranging from 5 to 54. The majority of departments (7 out of 11) demonstrated continuous efficiency improvements throughout the study period. The Surgery Department exhibited exceptional efficiency growth (183% increase), likely attributable to innovative practices and optimized resource management. Conversely, the Department of Infectious Diseases recorded the most substantial efficiency decline (-28.6%), potentially associated with post-COVID-19 pandemic epidemiological shifts. The Department of Ophthalmology and Optometry showed consistent efficiency reduction across all three years, indicating a need for resource reallocation and operational model optimization.
The scientific contribution of this research lies in developing a robust yet straightforward model for evaluating healthcare department performance that integrates fundamental input and output variables. The empirical findings provide evidence-based guidelines for resource management improvement and strategic interventions. Key limitations include the model’s simplicity, which excludes qualitative aspects of healthcare delivery and other variables that might further explain interdepartmental differences. Future research directions should incorporate additional variables such as service quality indicators, patient satisfaction metrics, and case complexity measures to create a more comprehensive performance assessment. Furthermore, methodological advancements could address DEA limitations through case-mix adjustments accounting for departmental heterogeneity, benchmarking against similar hospitals, and combining quantitative analysis with qualitative research methods to better understand organizational factors influencing efficiency.
This study contributes to understanding efficiency dynamics within hospital systems, offering empirically grounded insights for healthcare resource management improvement while establishing a foundation for future research addressing identified limitations and enhancing healthcare system efficiency evaluation models.