Efficiency in radiology departments: a two-stage analysis

Filippo Visintin (), Davide Aloini (), Simone Gitto (), Sara Vannelli () and Daniele Feo ()
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Filippo Visintin: University of Florence
Davide Aloini: University of Pisa, Largo Lucio Lazzarino
Simone Gitto: University of Siena
Sara Vannelli: University of Florence
Daniele Feo: Meyer Children’s Hospital IRCCS

Flexible Services and Manufacturing Journal, 2024, vol. 36, issue 2, No 4, 502 pages

Abstract: Abstract 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.

Keywords: Bootstrap DEA; Bootstrap truncated regression; Radiology; Efficiency (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10696-023-09499-6

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