Readiness of big health data analytics by technology-organization-environment (TOE) framework in Ethiopian health sectors
Journal Article
Bayou Tilahun Assaye a,*, Bekalu Endalew b, Maru Meseret Tadele a, Gizaw hailiye Teferie a, Abraham Teym c, Yidersal hune Melese d, Andualem fentahun senishaw a, Sisay Maru Wubante e, Habtamu Setegn Ngusie f, Aysheshim Belaineh Haimanot
Submitted: Sep 27, 2024
Issued: Date not specified
College of Health Science
Health Informatics
Abstract Preview:
Background: Big health data is a large and complex dataset that the health sector has collected andstored continuously to generate healthcare evidence for intervening the future healthcare un-certainty. However, data use for decision-making practices has been significantly low in devel-oping countries, especially in Ethiopia. Hence, it is critical to ascertain which elements influencethe health sectorâs decision to adopt big health data analytics in health sectors. The aim of thisstudy was to identify the level of readiness for big health data analytics and its associated factorsin healthcare sectors.Methods: A cross-sectional study design was conducted among 845 target employees using thestructural equation modeling approach by using technological, organizational, and environ-mental (TOE) frameworks. The target population of the study was health sector managers, di-rectors, team leaders, healthcare planning officers, ICT/IT managers, and health professionals.For data analysis, exploratory factor analysis using SPSS 20.0 and structural equation modelingusing AMOS software were used.Result: 58.85 % of the study participants had big health data analytics readiness. Complexity (CX),Top management support (TMS), training (TR) and government law policies and legislation(GLAL) and government IT policies (GITP) had positive direct effect, compatibility (CT), andoptimism (OP) had negative direct effect on BD readiness (BDR)Conclusion: The technological, organizational, and environmental factors significantly contributedto big health data readiness in the healthcare sector. The Complexity, compatibility, optimism,Top management support, training (TR) and government law and IT policies (GITP) had effect onbig health data analytics readiness. Formulating efficient reform in healthcare sectors, especially
or evidence-based decision-making and jointly working with stakeholders will be more relevantfor effective implementation of big health data analytics in healthcare sectors.
Keywords: Big health data, Data analytics, Data management, Health information revolution, Health sectors, Readiness
Full Abstract:
Background: Big health data is a large and complex dataset that the health sector has collected andstored continuously to generate healthcare evidence for intervening the future healthcare un-certainty. However, data use for decision-making practices has been significantly low in devel-oping countries, especially in Ethiopia. Hence, it is critical to ascertain which elements influencethe health sectorâs decision to adopt big health data analytics in health sectors. The aim of thisstudy was to identify the level of readiness for big health data analytics and its associated factorsin healthcare sectors.Methods: A cross-sectional study design was conducted among 845 target employees using thestructural equation modeling approach by using technological, organizational, and environ-mental (TOE) frameworks. The target population of the study was health sector managers, di-rectors, team leaders, healthcare planning officers, ICT/IT managers, and health professionals.For data analysis, exploratory factor analysis using SPSS 20.0 and structural equation modelingusing AMOS software were used.Result: 58.85 % of the study participants had big health data analytics readiness. Complexity (CX),Top management support (TMS), training (TR) and government law policies and legislation(GLAL) and government IT policies (GITP) had positive direct effect, compatibility (CT), andoptimism (OP) had negative direct effect on BD readiness (BDR)Conclusion: The technological, organizational, and environmental factors significantly contributedto big health data readiness in the healthcare sector. The Complexity, compatibility, optimism,Top management support, training (TR) and government law and IT policies (GITP) had effect onbig health data analytics readiness. Formulating efficient reform in healthcare sectors, especially
or evidence-based decision-making and jointly working with stakeholders will be more relevantfor effective implementation of big health data analytics in healthcare sectors.
Keywords: Big health data, Data analytics, Data management, Health information revolution, Health sectors, Readiness