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

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.