An Evaluation of BLU Hospital Efficiency: A Quantitative Approach with Data Envelopment Analysis
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Abstract
Research Originality — This study is the first to assess the efficiency of Public Service Agency (BLU) hospitals in Indonesia using data envelopment analysis (DEA). It provides a comprehensive evaluation of hospital efficiency in multiple categories and identification of benchmark hospitals and areas for improvement. The study offers insights into hospital resource management efficiency to help policymakers in optimizing hospital performance.
Research Objectives — This study aims to evaluate the efficiency of 32 BLU hospitals in Indonesia by analyzing their inpatient and outpatient services, human resource allocation, and bed utilization efficiency. It also investigates historical performance trends from 2016 to 2020 to assess long-term efficiency patterns.
Research Methods — The study employed DEA, which is a non-parametric approach widely used for efficiency analysis. The evaluation was based on four input variables and seven output variables categorized into four main efficiency measures: inpatient services, outpatient services, human resources, and bed utilization. The efficiency scores were calculated using BCC-I and Super-Radial BCC-I models.
Empirical Results — The findings showed that 15 hospitals were efficient, while 17 hospitals exhibited inefficiencies. Nine hospitals consistently demonstrated efficiency across all categories from 2016 to 2020, whereas four hospitals consistently underperformed in at least one category. The study also indicates that hospitals with lower efficiency scores can benchmark against efficient hospitals to improve performance.
Implications — The findings of this study have policy implications for healthcare administrators and government agencies. The Directorate of BLU Financial Management Development can use the DEA results to guide hospital efficiency improvements. In addition, inefficient hospitals can use these findings to identify performance gaps and adopt best practices. Future studies could integrate other methods such as the Malmquist productivity index (MPI) or balanced scorecard (BSC) for a more comprehensive assessment.
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