TY - JOUR
T1 - Data-driven quality improvement in low-and middle-income country health systems
T2 - Lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia
AU - Wagenaar, Bradley H.
AU - Hirschhorn, Lisa R.
AU - Henley, Catherine
AU - Gremu, Artur
AU - Sindano, Ntazana
AU - Chilengi, Roma
AU - Hingora, Ahmed
AU - Mboya, Dominic
AU - Exavery, Amon
AU - Tani, Kassimu
AU - Manzi, Fatuma
AU - Pemba, Senga
AU - Phillips, James
AU - Kante, Almamy Malick
AU - Ramsey, Kate
AU - Baynes, Colin
AU - Awoonor-Williams, John Koku
AU - Bawah, Ayaga
AU - Nimako, Belinda Afriyie
AU - Kanlisi, Nicholas
AU - Jackson, Elizabeth F.
AU - Sheff, Mallory C.
AU - Kyei, Pearl
AU - Asuming, Patrick O.
AU - Biney, Adriana
AU - Ayles, Helen
AU - Mwanza, Moses
AU - Chirwa, Cindy
AU - Stringer, Jeffrey
AU - Mulenga, Mary
AU - Musatwe, Dennis
AU - Chisala, Masoso
AU - Lemba, Michael
AU - Mutale, Wilbroad
AU - Drobac, Peter
AU - Rwabukwisi, Felix Cyamatare
AU - Binagwaho, Agnes
AU - Gupta, Neil
AU - Nkikabahizi, Fulgence
AU - Manzi, Anatole
AU - Condo, Jeanine
AU - Farmer, Didi Bertrand
AU - Hedt-Gauthier, Bethany
AU - Sherr, Kenneth
AU - Cuembelo, Fatima
AU - Michel, Catherine
AU - Gimbel, Sarah
AU - Kariaganis, Marina
AU - Manuel, João Luis
AU - Napua, Manuel
AU - Pio, Alusio
N1 - Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/21
Y1 - 2017/12/21
N2 - 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."
AB - 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."
KW - Data assessment
KW - Decision making
KW - Health systems research
KW - Health systems strengthening
KW - Low income
KW - Maternal and child health
KW - Mozambique
KW - Quality improvement
KW - Rwanda
KW - Zambia
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U2 - 10.1186/s12913-017-2661-x
DO - 10.1186/s12913-017-2661-x
M3 - Article
C2 - 29297319
AN - SCOPUS:85039054013
SN - 1472-6963
VL - 17
JO - BMC health services research
JF - BMC health services research
M1 - 830
ER -