Classification of medical imaging technologies: results from Türkiye
Abstract Background Regional disparities in access to medical diagnostic imaging technologies (MDITs) present a significant barrier to achieving health equity, particularly in developing countries. Understanding how these technologies are distributed and utilized is essential for informing equitable...
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BMC
2025-07-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-025-12997-y |
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| author | Hakan Temiz Tuncay Kara |
| author_facet | Hakan Temiz Tuncay Kara |
| author_sort | Hakan Temiz |
| collection | DOAJ |
| description | Abstract Background Regional disparities in access to medical diagnostic imaging technologies (MDITs) present a significant barrier to achieving health equity, particularly in developing countries. Understanding how these technologies are distributed and utilized is essential for informing equitable health policy. Method This study examines the distribution and utilization of MDITs across Türkiye’s 12 NUTS regions using a hierarchical clustering approach. Unlike previous studies, the analysis incorporates both technological capacity and utilization (CaU) variables, evaluated jointly and independently. Imaging modalities are also stratified based on their technological complexity and investment requirements to capture nuanced regional patterns. Results Findings indicate that although Türkiye demonstrates an overall balanced distribution of MDITs, notable regional disparities in utilization efficiency remain. Regions exhibiting similar usage patterns tend to cluster together irrespective of geographic proximity. Interestingly, the clusters often transcend geographical proximity; regions located at opposite ends of the country tend to cluster on the basis of similar utilization patterns. This may suggest that disparities between administrative centers and rural areas are less pronounced than previously assumed. These patterns imply that institutional capacity, healthcare workforce distribution, and demographic demand may have a stronger influence on utilization than spatial location. Conclusion The study highlights a disconnect between capacity and actual use of diagnostic imaging technologies, underscoring the need for targeted policy interventions. It also suggests that regional utilization patterns may align more with functional similarities than with geographic proximity. Moreover, analyzing technological capacity and utilization variables separately—rather than as a combined index—yielded more transparent and objective insights into regional disparities. These findings contribute to optimizing health resource allocation and support evidence-based policymaking aimed at advancing equitable access to diagnostic services, aligning with Türkiye’s commitment to universal health coverage. |
| format | Article |
| id | doaj-art-b639a6d04ac84fa99f4d2d2a51dba9cd |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-b639a6d04ac84fa99f4d2d2a51dba9cd2025-08-20T03:45:27ZengBMCBMC Health Services Research1472-69632025-07-0125111710.1186/s12913-025-12997-yClassification of medical imaging technologies: results from TürkiyeHakan Temiz0Tuncay Kara1Department of Computer Engineering, Artvin Coruh UniversityHealth Administration DepartmentAbstract Background Regional disparities in access to medical diagnostic imaging technologies (MDITs) present a significant barrier to achieving health equity, particularly in developing countries. Understanding how these technologies are distributed and utilized is essential for informing equitable health policy. Method This study examines the distribution and utilization of MDITs across Türkiye’s 12 NUTS regions using a hierarchical clustering approach. Unlike previous studies, the analysis incorporates both technological capacity and utilization (CaU) variables, evaluated jointly and independently. Imaging modalities are also stratified based on their technological complexity and investment requirements to capture nuanced regional patterns. Results Findings indicate that although Türkiye demonstrates an overall balanced distribution of MDITs, notable regional disparities in utilization efficiency remain. Regions exhibiting similar usage patterns tend to cluster together irrespective of geographic proximity. Interestingly, the clusters often transcend geographical proximity; regions located at opposite ends of the country tend to cluster on the basis of similar utilization patterns. This may suggest that disparities between administrative centers and rural areas are less pronounced than previously assumed. These patterns imply that institutional capacity, healthcare workforce distribution, and demographic demand may have a stronger influence on utilization than spatial location. Conclusion The study highlights a disconnect between capacity and actual use of diagnostic imaging technologies, underscoring the need for targeted policy interventions. It also suggests that regional utilization patterns may align more with functional similarities than with geographic proximity. Moreover, analyzing technological capacity and utilization variables separately—rather than as a combined index—yielded more transparent and objective insights into regional disparities. These findings contribute to optimizing health resource allocation and support evidence-based policymaking aimed at advancing equitable access to diagnostic services, aligning with Türkiye’s commitment to universal health coverage.https://doi.org/10.1186/s12913-025-12997-yHealth equityDiagnostic imagingRegional disparitiesHealthcare utilizationHierarchical clusteringCapacity and use |
| spellingShingle | Hakan Temiz Tuncay Kara Classification of medical imaging technologies: results from Türkiye BMC Health Services Research Health equity Diagnostic imaging Regional disparities Healthcare utilization Hierarchical clustering Capacity and use |
| title | Classification of medical imaging technologies: results from Türkiye |
| title_full | Classification of medical imaging technologies: results from Türkiye |
| title_fullStr | Classification of medical imaging technologies: results from Türkiye |
| title_full_unstemmed | Classification of medical imaging technologies: results from Türkiye |
| title_short | Classification of medical imaging technologies: results from Türkiye |
| title_sort | classification of medical imaging technologies results from turkiye |
| topic | Health equity Diagnostic imaging Regional disparities Healthcare utilization Hierarchical clustering Capacity and use |
| url | https://doi.org/10.1186/s12913-025-12997-y |
| work_keys_str_mv | AT hakantemiz classificationofmedicalimagingtechnologiesresultsfromturkiye AT tuncaykara classificationofmedicalimagingtechnologiesresultsfromturkiye |