Efficiency in radiology departments: a two-stage analysis

This study presents a novel application of two-stage bootstrap data envelopment analysis (DEA) to assess the efficiency of radiology units in public hospitals. In the first stage of our analysis, we carry out a bootstrap DEA analysis, testing the hypotheses of variable returns to scale to identify efficient units. In the second stage, we examine the effects of four explanatory variables (chief radiology technologist’s years of service, presence of customer satisfaction survey systems, age of the equipment, and output variety) other than inputs and outputs on the technical efficiency of radiology units using a bootstrap truncated regression. Counterintuitively, the results show that on average, the use of newer equipment leads to a decrease in productivity and that a chief radiology technologist with greater seniority and the presence of customer satisfaction survey systems do not translate into better productivity. Instead, productivity is positively influenced by the variety of a unit’s output.