The use of theories, models and frameworks to institutionalise routine data-driven decision-making in sub-Saharan African health facilities: a scoping review

Abstract Background Limited use of routine data for decision-making plagues Sub-Saharan African (SSA) health facilities despite key investments in health system data infrastructure to harness routine health-data. Evidence remains limited on how theories, models, and frameworks (TMFs) guide, inform, and assess efforts to institutionalise routine data-driven decision-making in SSA health facilities. This review aimed to determine which TMFs have been used to institutionalise routine data-driven decision-making in SSA health facilities and examine how they have been applied. Methods We searched six databases (MEDLINE, Scopus, Web of Science, EMBASE, Global Health, and CINAHL). Publications on data use in SSA health facilities were included. Extracted data included year of publication, study location, study design and intervention type. We conducted framework analysis to assess TMFs use in strengthening data use for decision-making in SSA health facilities. Results 44 papers (Qualitative = 23, quantitative = 7, mixed methods = 14) published between 2007 and 2025 across fourteen SSA countries were included. The fourteen identified TMFs for institutionalising routine data-driven decision-making were mostly determinant frameworks (29/44, 65.9%) which explored behavioural, technical and organisational data use factors. Few studies explained factor interaction over time to institutionalise data use. Common reported decision forums were performance monitoring (n = 13/44) and data/audit review (n = 7/44) meetings with 10/44 studies having unspecified decision-making forums. When featured, routine data use was operational (logistics, service delivery monitoring, target setting). Conclusion Determinant frameworks dominated the TMFs evidence-base of efforts to institutionalise routine data-driven decision-making and seldom explained their interaction over time. Institutionalisation requires understanding routine data use as a dynamic sociotechnical process where technologies, workflows, organisational structures and human behaviours interact over time. Routine data use across SSA health facilities remains operational rather than integrated into strategic and clinical decision-making. There’s need for systems thinking to identify better ways to leverage TMFs to strengthen routine data-driven decision-making eco-systems in SSA health facilities.