Digital health infrastructure for a Learning Health System: assessing how a national electronic health record supports adaptive frontline care for multiple long-term conditions in Zimbabwe
1
Global Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
2
Strategic Information and Research, Organization for Public Health Interventions and Development, Harare, Zimbabwe
Popul. Med. 2026;8(Supplement Supplement 1):A918
ABSTRACT
INTRODUCTION:
National electronic health records (EHR) are increasingly positioned as foundational to learning health systems (LHS), particularly in low- and middle-income countries where routine data are expected to support adaptive responses to growing multimorbidity (1,2). However, there is limited empirical evidence on whether EHRs enable learning generated in routine care to accumulate into system-level adaptation.
METHODS:
We conducted a qualitative, multi-method assessment of Impilo, Zimbabwe’s EHR, to examine whether it supports learning for integrated multimorbidity care at primary care level. Data were drawn from documentary review, ethnographic observation, patient journey mapping, and interviews with frontline providers, facility managers, and system actors. Analysis was guided by LHS theory, focusing on the conditions required for learning to translate from routine practice into sustained system adaptation (3).
RESULTS:
Learning was actively produced in routine care through frontline interpretation of patient information, experiential adaptation, and coordination across conditions. However, this learning remained largely encounter-bound and weakly stabilised within shared system artefacts. Impilo did not routinely provide usable longitudinal representations, prospective analytic tools for collective deliberation (4). As a result, learning was predominantly confined to adhering to workflows and guidelines, with limited capacity to inform changes to care pathways, routines, or system design which is critical for multimorbidity management.
CONCLUSIONS:
This study shows that the central constraint to building LHS through EHR is not the absence of learning, data, or frontline capability, but the lack of socio-technical arrangements that allow learning to accumulate, stabilize, and inform system-level adaptation(5). By diagnosing how learning is constrained within the Impilo ecosystem, the analysis clarifies why digitalization alone is insufficient to support adaptive care for people living with multimorbidity. Co-production with frontline health workers emerges as a critical pathway for reconnecting experiential learning to EHR design enabling cumulative and deliberative system learning (6).
National electronic health records (EHR) are increasingly positioned as foundational to learning health systems (LHS), particularly in low- and middle-income countries where routine data are expected to support adaptive responses to growing multimorbidity (1,2). However, there is limited empirical evidence on whether EHRs enable learning generated in routine care to accumulate into system-level adaptation.
METHODS:
We conducted a qualitative, multi-method assessment of Impilo, Zimbabwe’s EHR, to examine whether it supports learning for integrated multimorbidity care at primary care level. Data were drawn from documentary review, ethnographic observation, patient journey mapping, and interviews with frontline providers, facility managers, and system actors. Analysis was guided by LHS theory, focusing on the conditions required for learning to translate from routine practice into sustained system adaptation (3).
RESULTS:
Learning was actively produced in routine care through frontline interpretation of patient information, experiential adaptation, and coordination across conditions. However, this learning remained largely encounter-bound and weakly stabilised within shared system artefacts. Impilo did not routinely provide usable longitudinal representations, prospective analytic tools for collective deliberation (4). As a result, learning was predominantly confined to adhering to workflows and guidelines, with limited capacity to inform changes to care pathways, routines, or system design which is critical for multimorbidity management.
CONCLUSIONS:
This study shows that the central constraint to building LHS through EHR is not the absence of learning, data, or frontline capability, but the lack of socio-technical arrangements that allow learning to accumulate, stabilize, and inform system-level adaptation(5). By diagnosing how learning is constrained within the Impilo ecosystem, the analysis clarifies why digitalization alone is insufficient to support adaptive care for people living with multimorbidity. Co-production with frontline health workers emerges as a critical pathway for reconnecting experiential learning to EHR design enabling cumulative and deliberative system learning (6).
| eISSN: | 2654-1459 |
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