TY - GEN
T1 - ENHANCING HEALTHCARE MANAGEMENT IN LMICS THROUGH BIG DATA
T2 - 2024 International Annual Conference of the American Society for Engineering Management and 45th Annual Meeting: Engineering Management Riding the Waves of Smart Systems, ASEM 2024
AU - Telukdarie, Arnesh
AU - Mwanza, Joseph
N1 - Publisher Copyright:
Copyright © American Society for Engineering Management, 2024.
PY - 2024
Y1 - 2024
N2 - The potential of big data to revolutionize healthcare in low- and middle-income countries (LMICs) is hindered by challenges such as limited data availability and poor alignment with local healthcare needs. This study critically analyses the current state of big data in LMIC healthcare and develops a framework to enhance data utility while ensuring contextual relevance and user-friendliness. The methodology involves examining successful big data applications and identifying best practices in data quality improvement, interoperability, and capacity building within digital healthcare systems. The findings offer practical insights for optimising big data analytics in LMICs, addressing issues such as data incompatibility with local healthcare practices and infrastructure constraints. The expected impact includes guiding policymakers and healthcare providers in LMICs towards more effective big data implementations, aiming to improve healthcare delivery and patient outcomes. This study contributes to the advancement of equitable and efficient healthcare systems in LMICs by focusing on tailored, sustainable big data solutions.
AB - The potential of big data to revolutionize healthcare in low- and middle-income countries (LMICs) is hindered by challenges such as limited data availability and poor alignment with local healthcare needs. This study critically analyses the current state of big data in LMIC healthcare and develops a framework to enhance data utility while ensuring contextual relevance and user-friendliness. The methodology involves examining successful big data applications and identifying best practices in data quality improvement, interoperability, and capacity building within digital healthcare systems. The findings offer practical insights for optimising big data analytics in LMICs, addressing issues such as data incompatibility with local healthcare practices and infrastructure constraints. The expected impact includes guiding policymakers and healthcare providers in LMICs towards more effective big data implementations, aiming to improve healthcare delivery and patient outcomes. This study contributes to the advancement of equitable and efficient healthcare systems in LMICs by focusing on tailored, sustainable big data solutions.
KW - Big data
KW - Electronic Health Record
KW - Healthcare
KW - Interoperability
KW - Universal Health Coverage
UR - http://www.scopus.com/inward/record.url?scp=85219222677&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85219222677
T3 - Proceedings of the 2024 International Annual Conference and 45th Annual Meeting: Engineering Management Riding the Waves of Smart Systems, ASEM 2024
SP - 69
EP - 78
BT - Proceedings of the 2024 International Annual Conference and 45th Annual Meeting
A2 - Natarajan, Ganapathy
A2 - Zhang, Hao
A2 - Ng, Ean
PB - American Society for Engineering Management
Y2 - 6 November 2024 through 9 November 2024
ER -