Multimorbidity and Learning Health Systems: Optimising Data-to-Action (OptiMuL)

Multimorbidity, or the co-occurrence of two-or-more diseases in one person, is among the most significant emerging challenges for health systems globally. Multimorbidity indexes the breakdown of health systems organised around single diseases, which in many African nations translates into siloed organisation of care, fuelled by ‘vertical’ single-disease-centred programming. Key to responding to multimorbidity and its associated complexity – indeed, to many complex health system challenges – is capacity to generate and use robust, locally-relevant knowledge. ‘Learning health systems’ is a novel framework developed within health policy and systems research (HPSR) that elevates the importance of investing in practical learning across entrenched hierarchies and siloes of knowledge. Taking forward the synergies between multimorbidity and learning health systems conversations, OptiMuL is a cross-disciplinary, co-productive research programme that brings together academics, policymakers, and practitioners in Zimbabwe through a pilot multimorbidity learning hub to enhance understandings and system capabilities for addressing multimorbidity in Zimbabwe. Collapsing ‘evidencing’ and ‘intervening’ through the framework of learning, OptiMuL will simultaneously evidence and contribute towards a multimorbidity-responsive health system in Zimbabwe. In doing so, it provides insights into how knowledge and practice might synergise within a genuinely ‘person-centred’ system and the processes through which such systems can be realized.