Measuring the Efficiency of Qom Hospitals with Data Envelopment Analysis and Analytic Hierarchy Process

Abstract

Introduction: Due to increased costs of health sector, much effort has been done to compare the performance of hospitals and measure their efficiency in recent years. The purpose of this study was to evaluate the relative efficiency of public and private hospitals in Qom during 2007 using data envelopment analysis (DEA) approach. Methods: This was an applied and analytic study. The study population included all the 8 hospitals in Qom. There was no need to assess the validity and reliability since real data and performance statistics of hospitals were used. Input variables in this study were number of general practitioners, specialist physicians, and paramedical staff, as well as number of active beds. Output variables included number of inpatients and outpatients admitted. First, input variables weighted by analytical hierarchy process (AHP) and reduced to two variables, namely number of physicians and number of active beds. Then, input-oriented CCR (constant returns to scale (CRS)) and BCC (variable returns to scale (VRS)) were used to assess the performance of Qom hospitals in 2007. After determining inefficient hospitals, efficient hospitals were ranked using Anderson-Peterson (AP) model. Expert Choice, Win4Deap and DEA-Master were used to perform the calculations. Results: CCR model indicated 3 hospitals as efficient and five hospitals as inefficient. The final ranking of hospitals based on this model was 1. Zahra; 2. Fatemi; 3. Izadi; 4. Vali-e-Asr; 5.Masoomeh; 6. Nekooyi; 7. Kamkar; and 8. Golpaygani. However, based on BCC model, four hospitals were efficient and four were inefficient. The final ranking was 1. Zahra; 2. Vali-e-Asr; 3. Izadi; 4. Fatemi; 5. Masoomeh; 6. Nekooyi; 7. Kamkar; and 8. Golpaygani. Conclusion: Most hospitals are not efficient. The performance of inefficient hospitals can be improved following the patterns used by reference hospitals and also by better financial and human resources management. Keywords: Hospitals; Analysis; Data Interpretation, Statistical; Performance Assessment.