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.

Automated summary

This study applies a two-stage bootstrap data envelopment analysis (DEA) to assess the efficiency of radiology units in public hospitals. The first stage involves a bootstrap DEA analysis to identify efficient units by testing the hypotheses of variable returns to scale. In the second stage, a bootstrap truncated regression is used to 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) on the technical efficiency of radiology units. Surprisingly, the results show that newer equipment leads to decreased productivity, while seniority of the chief radiology technologist and the presence of customer satisfaction survey systems do not contribute to better productivity. On the other hand, a variety of output positively influences productivity.