Browsing by Author "Gremu A"

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    Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia.
    (2017-Dec-21) Wagenaar BH; Hirschhorn LR; Henley C; Gremu A; Sindano N; Chilengi R; Health Alliance International, Seattle, WA, USA.; Department of Global Health, School of Public Health, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA. wagenaarb@gmail.com.; University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.; Health Alliance International, Beira, Mozambique.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; Partners in Health, Kigali, Rwanda.; Health Alliance International, Seattle, WA, USA. wagenaarb@gmail.com.; Department of Global Health, School of Public Health, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    BACKGROUND: Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation's African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries. METHODS: Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs. RESULTS: Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda. CONCLUSION: Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external "audit."