Development of an edge-based digital clinical workflow and data processing wizard for low-resource primary healthcare systems

Authors

  • Geletaw Sahle Tegenaw Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium and Faculty of Computing, JiT, Jimma University.
  • Demisew Amenu Department of Obstetrics and Gynecology. College of Health Science, Jimma University.
  • Girum Ketema Faculty of Computing, JiT, Jimma University
  • Frank Verbeke Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
  • Jan Cornelis Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium.
  • Bart Jansen Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium and imec, Kapeldreef 75, 3001 Leuven, Belgium.

DOI:

https://doi.org/10.12856/JHIA-2023-v10-i3-371

Abstract

In low resource settings, paperwork hampers the elaboration of a digital clinical workflow and data processing. As a result, some of the challenges in a low-resource setting are related to obtaining historical records from a manual system (i.e., clinical guidelines, point of care charts, and other contextual documents), missing card-sheet information, and deficient readability of handwriting. Furthermore, limited infrastructure, resource constraints, deficient data readiness, and bridging the divide between evidence and practice are posing additional challenges. A WEB-APP clinical decision system (CDS) was developed and deployed on a Raspberry Pi 4 Model B, which has a quad-core 64-bit processor and 4GB of RAM. The Raspberry Pi 4 is intended to work with a power bank when there is no electricity in remote areas. The CDS instrument is accessed via a smart phone's mobile data or wireless network. Then, the system generates a digital clinical workflow and data processing wizard based on measured symptoms. The method improves the quality of existing clinical pathways by dynamically mapping a knowledgebase to data-driven methods. As a result, the CDS WEB-APP was able to provide a point-of-care clinical reference, data processing, and workflow generator, as well as an interactive data visualization and clinical guidance wizard for low-resource settings.

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Published

2024-08-06

How to Cite

[1]
Tegenaw, G.S. et al. 2024. Development of an edge-based digital clinical workflow and data processing wizard for low-resource primary healthcare systems. Journal of Health Informatics in Africa. 10, 3 (Aug. 2024), 1–8. DOI:https://doi.org/10.12856/JHIA-2023-v10-i3-371.

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