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. 2017 Dec 21;17(Suppl 3):830.
doi: 10.1186/s12913-017-2661-x.

Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia

Bradley H Wagenaar  1   2 , Lisa R Hirschhorn  3   4 , Catherine Henley  5   6 , Artur Gremu  7 , Ntazana Sindano  8 , Roma Chilengi  8   9 ; AHI PHIT Partnership Collaborative
Collaborators, Affiliations

Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia

Bradley H Wagenaar et al. BMC Health Serv Res. .

Abstract

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."

Keywords: Data assessment; Decision making; Health systems research; Health systems strengthening; Low income; Maternal and child health; Mozambique; Quality improvement; Rwanda; Zambia.

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Conflict of interest statement

Authors’ information

Bradley H. Wagenaar, MPH, PhD; Lisa R. Hirschhorn, MD, MPH; Catherine Henley, MPH; Artur Gremu; Ntazana Sindano; Roma Chilengi, MBChB, MS.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Modified Plan, Do, Study, Act framework used to inform development and implementation of data-driven QI approaches across the three study countries (Mozambique, Rwanda, Zambia)
Fig. 2
Fig. 2
Hirschhorn Partners In Health framework for data utilization for QI stages, or “ladder”, building on Berwick’s coping with data [23]. New stages are italicized
See this image and copyright information in PMC

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