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Updated: May 14, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Improving community health centres with big data analytics: A systematic literature review on adoption
Pascal Ndikuyeze1, Phahlane Mampilo1
1College of Science, Engineering and Technology- School of Computing, University of South Africa (UNISA), Pretoria, South Africa.
Introduction:
Implementing Big Data Analytics (BDA) could enhance the efficiency and effectiveness of Community Health Centres (CHCs). This study focuses on improving healthcare service delivery in CHCs located in the Nkangala District through the adoption of BDA. It identifies a specific research gap and seeks to consolidate existing knowledge while revealing challenges and opportunities in BDA implementation.
Methods:
This literature review was conducted using a systematic method that adheres to PRISMA principles. The review process involved the identification and selection of peer-reviewed publications in English up to 2024. The search was carried out among several major academic databases, including PubMed, Taylor & Francis Online, Google Scholar, IEEE Xplore, SpringerLink, ScienceDirect, and JSTOR. Specific search terms related to data-driven approaches and healthcare service delivery were used. The inclusion criteria focused on studies addressing the adoption and implementation of BDA in CHCs, while exclusion criteria eliminated studies not relevant to this context. The selected studies were analysed to assess the research state, identify key themes, and highlight gaps and challenges in BDA adoption within CHCs.
Results:
A total of 31 studies met the inclusion criteria, demonstrating variability in study design, geographic location, and focus areas related to BDA adoption and implementation. The synthesis of results unveiled common challenges, best practices, and outcomes associated with BDA implementation, including technological, organizational, and human factors influencing successful integration.
Conclusion:
The development and implementation of data-driven methodologies in healthcare service delivery present several challenges, including evidence limitations such as heterogeneity in study designs, restricted generalizability, and variability in study quality. Additionally, the short duration of many studies complicates the evaluation of their long-term impacts. Despite these challenges, the transformative potential of data-driven approaches highlights the necessity for further research to enhance adoption strategies and address existing research gaps.
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