Localized Data for Decision-Making: Implementing a Nurse-Led Data System for Maternal and Newborn Health in Tanzania

Nurse-Led Data System for Maternal and Newborn Health in Tanzania



maternal and newborn health, health management information systems (HMIS), quality improvement, Tanzania


Background: Health information systems are integral tools that strengthen (and ideally drive) decision-making related to delivery of healthcare services and health outcomes in lower-income countries. However, often these data systems remain underutilized in local healthcare facilities. In sub-Saharan Africa, many countries are seeing an increasing number of facility-based births but not necessarily a corresponding improvement in outcomes, indicating that maternal and newborn health is an area in which better health information is needed to inform quality improvement initiatives. The purpose of this project was to design, implement, and evaluate a localized HIS to build capacity for quality improvement and research in maternal and newborn health at Muhimbili National Hospital in Dar es Salaam, Tanzania.

Methods: Through a collaborative partnership between Muhimbili National Hospital and Emory University, a data collection system using REDCap was developed to provide sustainable, high-quality data on in-patient maternal and newborn health services. Called the Obstetric and Neonatal Database, this project was led and implemented by nurses at MNH with support from staff obstetricians, IT personnel, and the hospital’s Training, Research, and Consultancy Unit. Four months after its launch, we conducted a mixed-methods evaluation that used quantitative methods to assess data capture and quality, and qualitative methods to elicit perceptions and experiences of users.

Results: The project demonstrated significant successes overall and continues to be used as a means of assessing quality on the maternity wards today. Although data accuracy was high, the evaluation revealed large discrepancies in data capture; specifically, data from labor and surgical wards were >97% complete versus only about 20% complete in postnatal and newborn wards. This inconsistency is attributed to differing degrees of hands-on training and efforts to promote ownership and investment among nursing staff. On the other hand, most nurses overwhelmingly reported positive experiences with the database, describing unanticipated benefits that ranged from enhanced workflow efficiency to improved data security to updated technology-related skills.

Conclusion: To effectively provide local health facilities with critical information for evaluating and improving outcomes, health management information systems must be closely tailored to the needs of specific contexts and for the benefit of all users.


World Health Organization. Standards for Improving Quality of Maternal and Newborn Care in Health Facilities 2016.

Abajebel S, Jira C, Beyene W. Utilization of health information system at district level in jimma zone oromia regional state, South west ethiopia. Ethiopian journal of health sciences. 2011;21(Suppl 1):65-76. PubMed PMID: 22435010.

Avan BI, Berhanu D, Umar N, Wickremasinghe D, Schellenberg J. District decision-making for health in low-income settings: a feasibility study of a data-informed platform for health in India, Nigeria and Ethiopia. Health Policy and Planning. 2016;31(suppl_2):ii3-ii11. doi: 10.1093/heapol/czw082.

Wickremasinghe D, Hashmi IE, Schellenberg J, Avan BI. District decision-making for health in low-income settings: a systematic literature review. Health Policy and Planning. 2016;31(suppl_2):ii12-ii24. doi: 10.1093/heapol/czv124.

AbouZahr C, Boerma, T. Health Information System: the foundations of public health. Bulletin of the World Health Organization. 2005;83.

Bhattacharyya S, Berhanu D, Taddesse N, Srivastava A, Wickremasinghe D, Schellenberg J, et al. District decision-making for health in low-income settings: a case study of the potential of public and private sector data in India and Ethiopia. Health Policy and Planning. 2016;31(suppl_2):ii25-ii34. doi: 10.1093/heapol/czw017.

Chitama D, Baltussen R, Ketting E, Kamazima S, Nswilla A, Mujinja PGM. From papers to practices: district level priority setting processes and criteria for family planning, maternal, newborn and child health interventions in Tanzania. BMC Women's Health. 2011;11(1):46. doi: 10.1186/1472-6874-11-46.

Kimaro HC, Sahay S. An institutional perspective on the process of decentralization of health information systems: A case study from Tanzania. Information Technology for Development. 2007;13(4):363-90. doi: 10.1002/itdj.20066.

Mutale W, Chintu N, Amoroso C, Awoonor-Williams K, Phillips J, Baynes C, et al. Improving health information systems for decision making across five sub-Saharan African countries: Implementation strategies from the African Health Initiative. BMC Health Services Research. 2013;13(2):S9. doi: 10.1186/1472-6963-13-S2-S9.

Mutemwa RI. HMIS and decision-making in Zambia: re-thinking information solutions for district health management in decentralized health systems. Health Policy and Planning. 2005;21(1):40-52. doi: 10.1093/heapol/czj003.

Nutley T, McNabb S, Salentine S. Impact of a decision-support tool on decision making at the district level in Kenya. Health Research Policy and Systems. 2013;11(1):34. doi: 10.1186/1478-4505-11-34.

Ministry of Health and Social Welfare. The Tanzania Quality Improvement Framework in Health Care. 2011.

Wilms MC, Mbembela O, Prytherch H, Hellmold P, Kuelker R. An in-depth, exploratory assessment of the implementation of the National Health Information System at a district level hospital in Tanzania. BMC Health Services Research. 2014;14(1):91. doi: 10.1186/1472-6963-14-91.

Kidanto HL, Wangwe P, Kilewo CD, Nystrom L, Lindmark G. Improved quality of management of eclampsia patients through criteria based audit at Muhimbili National Hospital, Dar es Salaam, Tanzania. Bridging the quality gap. BMC Pregnancy and Childbirth. 2012;12(1):134. doi: 10.1186/1471-2393-12-134.

Macheku GS, Philemon RN, Oneko O, Mlay PS, Masenga G, Obure J, et al. Frequency, risk factors and feto-maternal outcomes of abruptio placentae in Northern Tanzania: a registry-based retrospective cohort study. BMC Pregnancy and Childbirth. 2015;15(1):242. doi: 10.1186/s12884-015-0678-x.

Ministry of Health and Social Welfare. Health Sector Strategic Plan III ‘Partnership for Delivering the MDGs’ 2009-2015. Dar es Salaam, Tanzania2009.

Ministry of Health and Social Welfare. Tanzania National eHealth Strategy 2012-2018. 2013.

Ministry of Health CD, Gender, Elderly and Children. Tanzania Demographic and Health Survey and Marlaria Indicator Survey,. 2016.

World Bank U, World Bank Group, and the United Nations Population Division,. Trends in Maternal Mortality: 2000 to 2017 Geneva: World Health Organization; 2019.

Mortality Rate, Neonatal [Internet]. UN Interagency Group for Child Mortality Estimation. 2018.

Births attended by skilled health staff (% of total) [Internet]. UNICEF, State of the World’s Children, Childinfo, and Demographic and Health Surveys. 2016.

Mgaya A, Hinju J, Kidanto H. Is time of birth a predictor of adverse perinatal outcome? A hospital-based cross-sectional study in a low-resource setting, Tanzania. BMC Pregnancy and Childbirth. 2017;17(1):184. doi: 10.1186/s12884-017-1358-9.

Kamala BA, Mgaya AH, Ngarina MM, Kidanto HL. Predictors of low birth weight and 24-hour perinatal outcomes at Muhimbili National Hospital in Dar es Salaam, Tanzania: a five-year retrospective analysis of obstetric records. Pan Afr Med J. 2018;29:220-. doi: 10.11604/pamj.2018.29.220.15247. PubMed PMID: 30100974.

Kidanto HL, Massawe SN, Nystrom L, Lindmark G. Analysis of perinatal mortality at a teaching hospital in Dar es Salaam, Tanzania, 1999-2003. African journal of reproductive health. 2006;10(2):72-80. Epub 2007/01/16. PubMed PMID: 17217119.

Mdegela MH, Muganyizi PS, Pembe AB, Simba DO, Van Roosmalen J. How rational are indications for emergency caesarean section in a tertiary hospital in Tanzania? Tanzania journal of health research. 2012;14(4):236-42. Epub 2012/10/01. PubMed PMID: 26591720.

Muganyizi PS, Kidanto HL. Impact of change in maternal age composition on the incidence of Caesarean section and low birth weight: analysis of delivery records at a tertiary hospital in Tanzania, 1999-2005. BMC pregnancy and childbirth. 2009;9:30-. doi: 10.1186/1471-2393-9-30. PubMed PMID: 19622146.

Mgaya AH. Improving the quality of caesarean section in a low-resource setting: An intervention by criteria-based audit at a tertiary hospital, Dar es Salaam, Tanzania: Acta Universitatis Upsaliensis; 2017.

Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics. 2009;42(2):377-81. doi: https://doi.org/10.1016/j.jbi.2008.08.010.

About REDCap 2020. Available from: https://projectredcap.org/about/.

VERBI Software. MAXQDA 2020. Berlin: VERBI Software; 2019.

Boldosser-Boesch A, Brun M, Carvajal L, Chou D, de Bernis L, Fogg K, et al. Setting maternal mortality targets for the SDGs. The Lancet. 2017;389(10070):696-7. doi: https://doi.org/10.1016/S0140-6736(17)30337-9.



How to Cite

Spangler, S. A., Siira, M. R., Mgaya, A. H., Lutavi, J. B., Murray, B. L., & Chiwanga, F. S. (2022). Localized Data for Decision-Making: Implementing a Nurse-Led Data System for Maternal and Newborn Health in Tanzania: Nurse-Led Data System for Maternal and Newborn Health in Tanzania. Journal of Health Informatics in Developing Countries, 16(2). Retrieved from https://www.jhidc.org/index.php/jhidc/article/view/367



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