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. 2025 Jun;28(2):207-233.
doi: 10.1007/s10729-025-09702-0. Epub 2025 May 2.

Visualisation of Data Envelopment Analysis in primary health services

Ane Elixabete Ripoll-Zarraga  1 , José Luis Franco Miguel  2 , Carmen Fullana Belda  2
Affiliations

Visualisation of Data Envelopment Analysis in primary health services

Ane Elixabete Ripoll-Zarraga et al. Health Care Manag Sci. 2025 Jun.

Abstract

Benchmark efficiency analysis in public health typically focuses on hospitals rather than primary care providers. Data Envelopment Analysis (DEA) is widely used to assess resource efficiency among decision-making units (DMUs). However, traditional DEA struggles to differentiate between efficient units and is sensitive to the selection of inputs and outputs. Methods like super-efficiency and cross-efficiency address some of these limitations but often exclude outliers and may overlook efficiency related to specialisation. DEA Visualisation integrates DEA with multivariate statistical methods allowing for the identification of inefficiency sources and specialisation patterns without losing discriminatory power or removing extreme cases from the sample. This study analyses 82 public primary health centres in Madrid serving senior citizens in 2018. The findings reveal inefficiencies such as a preference for prescribing specific rather than generic drugs, increasing public health costs. Additionally, two extreme cases (outliers or mavericks) were identified as having high infrastructure costs and disproportionate staffing. Redistributing patients from overcrowded centres could enhance efficiency, while centres focused on preventive care showed greater cost-effectiveness, particularly in reducing prescription costs.

Keywords: DEA Visualisation; Efficiency; Extreme Cases; Health Specialisation; Multivariate Statistical Analysis; Primary Health Care.

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Conflict of interest statement

Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Conflict of interest: The authors declare they have no financial interests.

Figures

Fig. 1
Fig. 1
Common Map. Factors 1 and 2
Fig. 2
Fig. 2
Common Map. Factors 3 and 4
Fig. 3
Fig. 3
ProFit DEA specifications projection (Dimension 1 and Dimension 2)
Fig. 4
Fig. 4
ProFit DEA specifications projection (Dimension 3 and Dimension 4)
Fig. 5
Fig. 5
ProFit for Dimensions 1 and 7
Fig. 6
Fig. 6
Dendrogram. Ward Method (Unstandardised)
Fig. 7
Fig. 7
Common Map. Cluster Analysis (Factors 3 and 4)
Fig. 8
Fig. 8
Common Map. Factors 5 and 6 (Rotated Solution)
Fig. 9
Fig. 9
Common Map. Factors 1 and 7 (Rotated Solution)
Fig. 10
Fig. 10
ProFit for Dimensions 5 and 6
Fig. 11
Fig. 11
ProFit for Dimensions 1 and 7
Fig. 12
Fig. 12
Common Map. Cluster Analysis (Factors 1 and 2)
Fig. 13
Fig. 13
Common Map. Cluster Analysis (Factors 5 and 6)
See this image and copyright information in PMC

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