Accessibility to health services, both in-person and virtual, is vital for advancing Health and Well-being SDG. While e-healthcare can bridge some of these gaps, its success depends on reliable digital infrastructure and coverage. This paper provides an integrated approach that considers both dimensions, physical and digital. We evaluate Multi-Edge Graph model, Multi-Layer Graph model, Composite index, and unsupervised learning approaches to measure physical and digital accessibility apply for South Africa. Our findings show that clustering methods, reinforced by robust imputation, afford the best balance between objectivity, interpretability, and actionable insights under the current data environment offering a scalable framework for assessing hybrid healthcare systems and provide valuable insights for policy, planning, and resource allocation.

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Measuring Healthcare Accessibility Through Clustering Techniques and Multi-layer Network Analysis

  • Dhananjay Balakrishnan,
  • Svea Drekshagen,
  • Vedant Sahu,
  • Mersedeh Tariverdi,
  • Miguel Nunez-del-Prado

摘要

Accessibility to health services, both in-person and virtual, is vital for advancing Health and Well-being SDG. While e-healthcare can bridge some of these gaps, its success depends on reliable digital infrastructure and coverage. This paper provides an integrated approach that considers both dimensions, physical and digital. We evaluate Multi-Edge Graph model, Multi-Layer Graph model, Composite index, and unsupervised learning approaches to measure physical and digital accessibility apply for South Africa. Our findings show that clustering methods, reinforced by robust imputation, afford the best balance between objectivity, interpretability, and actionable insights under the current data environment offering a scalable framework for assessing hybrid healthcare systems and provide valuable insights for policy, planning, and resource allocation.