A four-stage DEA-based efficiency evaluation of public hospitals in China after the implementation of new medical reforms.

Abstract

This study applied the non-parametric four-stage data envelopment analysis method (Four-Stage DEA) to measure the relative efficiencies of Chinese public hospitals from 2010 to 2016, and to determine how efficiencies were affected by eight factors. A sample of public hospitals (n = 84) was selected from Chongqing, China, including general hospitals and traditional Chinese medicine hospitals graded level 2 or above. The Four-Stage-DEA method was chosen since it enables the control of the impact of environment factors on efficiency evaluation results. Data on the number of staff, government financial subsidies, the number of beds and fixed assets were used as input whereas the number of out-patients and emergency department patients and visits, the number of discharged patients, medical and health service income and hospital bed utilization rate were chosen as study outputs. As relevant environmental variables, we selected GDP per capita, permanent population, population density, number of hospitals and number of available sickbeds in local medical institutions. The relative efficiencies (i.e. technical, pure technical, scale) of sample hospitals were also calculated to analyze the change between the first stage and fourth stage every year. The study found that Four-Stage-DEA can effectively filter the impact of environmental factors on evaluation results, which sets it apart from other models commonly used in existing studies.