Implementation of Big Data Analytics for Diabetes Management: Needs Assessment in the Rwanda Healthcare System

2026-05-26

AI Summary

This study addresses the limited capacity of Rwanda’s healthcare system to leverage big data analytics for early diabetes diagnosis and clinical decision-making. Through a series of multi-stakeholder workshops, the research systematically assesses the nation’s readiness for big data applications in healthcare and proposes, for the first time, an interpretable machine learning–driven diabetes management framework tailored to low-resource clinical settings. Integrating big data analytics, explainable AI models, and health information system interoperability, the framework identifies critical implementation barriers and capability gaps. Building on these insights, the study outlines a contextually feasible pathway and a technically grounded implementation strategy aligned with Rwanda’s healthcare realities, offering an innovative paradigm for intelligent chronic disease management in resource-constrained environments.

Abstract

Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and machine learning offer practical tools for analyzing large health datasets and supporting early detection and better treatment decisions. However, their use in routine clinical practice is still limited. This study examines the readiness of Rwanda's healthcare system to adopt big data analytics for diabetes management. As the country continues to expand its use of electronic medical records and health information systems, new opportunities arise for improving prediction, monitoring, and clinical decision-making. A five-day workshop involving 25 key stakeholders, including clinicians, data managers, policymakers, medical researchers, nutritionists, and technology providers, was conducted to assess preparedness and identify existing gaps. The findings highlight both the potential and the main challenges of BDA implementation. Based on these results, the paper proposes a practical BDA framework to support diabetes management strategies using explainable machine learning models.

Problem

• Big Data Analytics
• Diabetes Management
• Healthcare System
• Readiness Assessment
• Clinical Decision Support

Innovation

• Big Data Analytics
• Explainable Machine Learning
• Diabetes Management
• Healthcare Readiness Assessment
• Electronic Medical Records