Implementing, improving and learning: cross-country lessons learned from the African Health Initiative

Data-driven decision-making for district health management: a cluster-randomised study in 24 districts of Ethiopia

BACKGROUND

Use of local data for health system planning and decision-making in maternal, newborn and child health services is limited in low-income and middle-income countries, despite decentralisation and advances in data gathering. An improved culture of data-sharing and collaborative planning is needed. The Data-Informed Platform for Health is a system-strengthening strategy which promotes structured decision-making by district health officials using local data. Here, we describe implementation including process evaluation at district level in Ethiopia, and evaluation through a cluster-randomised trial.

METHODS

We supported district health teams in 4-month cycles of data-driven decision-making by: (a) defining problems using a health system framework; (b) reviewing data; (c) considering possible solutions; (d) value-based prioritising; and (e) a consultative process to develop, commit to and follow up on action plans. 12 districts were randomly selected from 24 in the North Shewa zone of Ethiopia between October 2020 and June 2022. The remaining districts formed the trial's comparison arm. Outcomes included health information system performance and governance of data-driven decision-making. Analysis was conducted using difference-in-differences.

RESULTS

58 4-month cycles were implemented, four or five in each district. Each focused on a health service delivery challenge at district level. Administrators' practice of, and competence in, data-driven decision-making showed a net increase of 77% (95% CI: 40%, 114%) in the regularity of monthly reviews of service performance, and 48% (95% CI: 9%, 87%) in data-based feedback to health facilities. Statistically significant improvement was also found in administrators' use of information to appraise services. Qualitative findings also suggested that district health staff reported enhanced data use and collaborative decision-making.

CONCLUSIONS

This study generated robust evidence that 20 months' implementation of the Data-Informed Platform for Health strengthened health management through better data use and appraisal practices, systemised problem analysis to follow up on action points and improved stakeholder engagement.

TRIAL REGISTRATION NUMBER

NCT05310682.

How does embedded implementation research work? Examining core features through qualitative case studies in Latin America and the Caribbean

Innovative strategies are needed to improve the delivery of evidence-informed health interventions. Embedded implementation research (EIR) seeks to enhance the generation and use of evidence for programme improvement through four core features: (1) central involvement of programme/policy decision-makers in the research cycle; (2) collaborative research partnerships; (3) positioning research within programme processes and (4) research focused on implementation. This paper examines how these features influence evidence-to-action processes and explores how they are operationalized, their effects and supporting conditions needed. We used a qualitative, comparative case study approach, drawing on document analysis and semi-structured interviews across multiple informant groups, to examine three EIR projects in Bolivia, Colombia and the Dominican Republic. Our findings are presented according to the four core EIR features. The central involvement of decision-makers in EIR was enhanced by decision-maker authority over the programme studied, professional networks and critical reflection. Strong research-practice partnerships were facilitated by commitment, a clear and shared purpose and representation of diverse perspectives. Evidence around positioning research within programme processes was less conclusive; however, as all three cases made significant advances in research use and programme improvement, this feature of EIR may be less critical than others, depending on specific circumstances. Finally, a research focus on implementation demanded proactive engagement by decision-makers in conceptualizing the research and identifying opportunities for direct action by decision-makers. As the EIR approach is a novel approach in these low-resource settings, key supports are needed to build capacity of health sector stakeholders and create an enabling environment through system-level strategies. Key implications for such supports include: promoting EIR and creating incentives for decision-makers to engage in it, establishing structures or mechanisms to facilitate decision-maker involvement, allocating funds for EIR, and developing guidance for EIR practitioners.