Qualitative research identifies contextual barriers to health information systems in underserved regions, suggesting strategies for improvement.
BACKGROUND Health information systems (HIS) are essential for strengthening health systems in underserved areas. However, many HIS in Africa are still in the early stages of implementation, and existing systems often suffer from imbalances in data availability. Their optimization faces various challenges, including limited resources, which restricts their scalability. OBJECTIVE The aim of this study is to identify contextual barriers that hinder the optimization of Health Information Systems (HIS) in underserved settings. Specifically, the study adopts the frameworks of Frugal Innovation (FI) and Information and Communication Technologies for Development (ICT4D) to explore ways to enhance the quality of healthcare delivery for low-income populations. METHODS A qualitative research approach was employed, involving 32 participants (n=32). The study was guided by the central theme: "Contextual barriers and challenges hindering the optimization of HIS. RESULTS Four major thematic categories emerged from the data: HIS contextualization, health system factors, service provider issues, and HIS integration. The findings offer valuable insights that can contribute to transforming HIS in underserved settings and improving healthcare quality. CONCLUSIONS Health Information Systems have the potential to significantly enhance healthcare delivery in underserved regions, such as those in Africa. However, progress has been limited due to poor digital infrastructure and other contextual barriers. These challenges undermine the performance and effectiveness of HIS interventions, ultimately impairing data-driven decision-making and leading to poor health outcomes. To overcome these obstacles, effective strategies rooted in ICT4D and Frugal Innovation should be adopted. This study provides actionable insights that can support the transformation of health systems in low- and middle-income countries (LMICs) by addressing critical contextual barriers to HIS optimization.
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Suoheimo et al. (2025) studied this question.
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