Multimorbidity and Learning Health Systems: Qualitative and Documentary Dataset on Electronic Health Records and Multimorbidity Care, Zimbabwe, 2025–2026

Multimorbidity, commonly defined as the coexistence of two or more long-term conditions in one person, is increasingly recognised as a major challenge for health systems globally. Across sub-Saharan Africa, the growing burden of non-communicable diseases (NCDs) is converging with persistent infectious diseases such as HIV, creating new demands for integrated, person-centred models of care. Yet many health systems remain organised around single diseases and programme-specific structures, limiting their ability to support coordinated care, collective learning and adaptation. Learning Health Systems (LHS) have emerged as one response to this challenge, emphasising the generation and use of information, deliberative processes through which knowledge is interpreted and acted upon, and practical learning embedded within routine service delivery and improvement efforts. However, there remains limited empirical evidence regarding how these learning capacities operate in resource-constrained settings and their potential to support multimorbidity care. This dataset was generated as part of a mixed-methods study examining the capacities required to support learning-oriented multimorbidity care in Zimbabwe. The dataset is drawn from Phase 1 of the Multimorbidity and Learning Health Systems: Optimising Data-to-Action (OptiMuL) programme, a mixed-methods analysis conducted January 2025 – March 2026. Using HIV and hypertension as tracer conditions, it explored how information, decision-making and practice interact to support coordinated, longitudinal and person-centred care. Particular attention was given to the role of Zimbabwe’s national Electronic Health Record (EHR), Impilo, as a key component of the wider learning infrastructure, alongside the organisational, governance and service-delivery processes through which information is translated into action. The study examined how information is generated, shared and used; how actors deliberate and make decisions; and how learning occurs within routine care and health system management. Data collection occurred in two urban primary healthcare facilities located in Chitungwiza and Bulawayo, alongside documentary review and stakeholder engagement activities undertaken at district and national levels. Four complementary methods were employed: documentary review, patient journey mapping, participant observation and in-depth interviews. Documentary review involved the analysis of policies, strategies, governance documents, programme guidance, evaluation reports and empirical studies relevant to multimorbidity, LHS and Zimbabwe’s digital health ecosystem (n=13 documents). Patient journey mapping involved ethnographic shadowing of patients receiving care for HIV, hypertension, diabetes, cancer and multimorbidity (n=21 patient journeys represented in (n=23) journey-mapping documents). Participant observation generated fieldnote summaries documenting routine service delivery, information use, coordination practices, EHR utilisation, workflow adaptations and service integration processes (n=21 fieldnote summaries). In-depth interviews were conducted with frontline clinicians, facility managers, programme personnel, technical specialists and national decision-makers involved in service delivery, digital health, health information systems and policy implementation (n=19 interview transcripts). The deposited data comprise a documentary analysis dataset containing documentary extraction, coding, readiness assessments and analytic interpretations (n=13 documents); anonymised interview transcripts (n=19); patient journey mapping documents (n=23 documents representing 21 observed patient journeys); fieldnote summaries (n=21); metadata catalogues describing interviews, patient journeys and fieldnotes (n=3 spreadsheets); supporting study documentation including interview guides and consent templates; and a protocol summary describing study design and implementation. The collection provides a unique empirical resource for understanding how health systems develop and deploy learning capacities for multimorbidity care in resource-constrained settings. It offers insight into the relationships between information systems, decision-making processes and practical learning, and opportunities and constraints facing efforts associated with supporting integrated, learning-oriented models of care in resource-constrained settings.