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Data-driven approach to assess and identify gaps in healthcare set up in South Asia

Abstract:
Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages [11]. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-981-96-7036-9_14

Authors


Publisher:
Springer Nature
Host title:
Neural Information Processing
Volume:
2297
Pages:
199-217
Series:
Communications in Computer and Information Science (CCIS)
Place of publication:
Singapore
Publication date:
2025-07-19
Acceptance date:
2024-08-21
Event title:
31st International Conference on Neural Information Processing (ICONIP) 2024
Event location:
Auckland, New Zealand
Event website:
https://iconip2024.org/
Event start date:
2024-12-02
Event end date:
2024-12-06
DOI:
EISSN:
1865-0937
ISSN:
1865-0929
EISBN:
978-981-96-7036-9
ISBN:
978-981-96-7035-2


Language:
English
Keywords:
Pubs id:
2279733
Local pid:
pubs:2279733
Deposit date:
2026-06-18
ARK identifier:

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