AI Insight
This mixed-methods study evaluated Zimbabwe's national electronic health record system (Impilo) and found that while frontline healthcare workers actively generate learning about patient care through their daily work with HIV and hypertension patients, this learning remains isolated to individual encounters and is not systematically captured or used to improve the broader healthcare system. The research revealed that Impilo lacks critical features such as longitudinal patient views, analytical tools for practitioners, and mechanisms for collective reflection that would enable frontline insights to inform system-wide improvements in multimorbidity care. The study concludes that digitalization alone is insufficient without socio-technical infrastructure that connects frontline learning to adaptive system change.
Why it matters
As chronic diseases and multimorbidity increase in Sub-Saharan Africa, this research identifies specific gaps in how digital health systems can be designed to support integrated care delivery. The findings suggest that effective electronic health records in low-resource settings must be co-designed with frontline workers to capture and systematize their learning, rather than simply digitizing existing workflows.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Electronic health records (EHR) are increasingly recognised as critical digital infrastructure for integrated, patient-centred care in the context of rising multimorbidity. In low-resource settings, national EHRs may also support locally driven learning to improve adaptive care across chronic conditions. However, there is limited empirical evidence on whether and how these systems enable learning within routine care in ways that inform broader system adaptation. We conducted a qualitative multi-method assessment of Impilo, Zimbabwe’s national EHR, to examine its capacity to support learning for integrated multimorbidity care at primary care level, using HIV-hypertension as a tracer condition pair. Guided by Friedman’s socio-technical infrastructure model as the analytical framework and Learning Health Systems (LHS) theory as the interpretive framework, data were drawn from documentary review, ethnographic observation, patient journey mapping, and interviews with frontline health workers and key stakeholders. Frontline learning for person-centred multimorbidity care was actively generated through interpretation of patient trajectories, experiential adjustment, and coordination across HIV and hypertension services using both the EHR and paper-based artefacts such as registers and patient booklets. However, this learning remained largely encounter-bound and weakly stabilised. Impilo did not routinely provide usable longitudinal patient views, practice-facing analytic tools, or institutionalised mechanisms for collective reflection required to support integrated multimorbidity care. Consequently, learning was largely confined to incremental adjustment within existing workflows, with limited capacity to inform broader changes to care pathways, routines, or system design. These findings suggest that the principal barrier to developing LHS is not the absence of data or frontline learning capacity, but the lack of socio-technical arrangements that enable learning to stabilise and inform system adaptation. Digitalisation alone is insufficient to support adaptive multimorbidity care. Co-production with frontline health workers may provide a pathway for aligning digital system design with routine care realities.