ArticleOxford open digital health2026
Surviving institutional contradictions: routine data use practices at the subnational levels.
Article in Oxford open digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Routine Health Information System data are widely promoted as a foundation for evidence-informed planning, supervision, and service improvement in low- and middle-income countries. Yet their use in decentralized health systems is often shaped by contradictory institutional logics. In this study, contradictory institutional logics refer to conflicting expectations about what actors should prioritize, how data should be used, and which resources are made available to do so. We examined how these contradictions shape routine data use at subnational levels of the Tanzanian health system and how local actors respond to them. We conducted a qualitative study in the Dodoma City Council and the Bahi District Council, Tanzania, between 2024 and 2025. Data were generated through 31 key informant interviews and nonparticipant observations across 12 purposively selected health facilities and two council health management teams. Participants included facility in-charges, health management information system focal persons, facility programme coordinators, and council program coordinators. Data were analyzed thematically using institutional theory as an interpretive lens. The findings from the two councils reveal that routine data use was not absent; rather, it was improvised within a contradictory institutional environment. Strengthening data use, therefore, requires more than technical deployment. It requires institutionalized procedures for review and supervision, broader analytic capacity at the facility level, better alignment between DHIS2 and GoT-HOMIS, and target-setting and feedback practices that are meaningful to local service realities. These findings are analytically relevant to similar decentralized settings, although they are not statistically generalizable beyond the study context.
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