Reengineered DHIS 2 to Capture Maternal and Child Data at Point of Service for Prompt Intelligent Decision Making and Data Visualisation: A Case of Kiambu County
DOI:
https://doi.org/10.61978/medicor.v3i4.1161Keywords:
Reengineered DHIS2, Maternal and Child Health, Data Quality, Big Data, Kiambu CountyAbstract
Reengineering of health information systems (HIS) involves restructuring system functionality to improve efficiency, usability, and reliability for rapid, evidence-based decision-making. This study presents a reengineered District Health Information Software 2 (DHIS2) prototype designed to capture maternal and child health (MCH) data at the point of service in Kiambu County, Kenya. By integrating key measures of data quality – namely completeness, timeliness, accuracy, and consistency – the prototype aligns with the four Vs of big data. A cross-sectional study involving 23 health facilities used questionnaires, interviews, and desk research to assess existing data management processes. Findings revealed heavy reliance on manual registers, resulting in duplicated entries, incomplete records, and delayed reporting. The reengineered DHIS2 prototype automated these processes, enabling real-time data capture and improved tracking of maternal and child health indicators. Testing at a pilot facility demonstrated improved attendance tracking, reduced data entry errors, and real-time dashboard and GIS analytics supporting decisions on ANC follow-up, HIV prevention, and maternal delivery outcomes. The number of mothers served per day increased from an average of 60–100 to 120–150, while reporting timeliness improved from 20% to over 95%. This study highlights the potential of customizing DHIS2 to strengthen maternal and child health data systems in resource-limited settings, offering a cost-effective alternative to proprietary electronic health records.
References
Abutabenjeh, S. (2018). Clarification of research design , research methods , and research methodology : A guide for public administration researchers and practitioners. https://doi.org/10.1177/0144739418775787 DOI: https://doi.org/10.1177/0144739418775787
Asah, F., Kanjo, C., Addo, H., Divine, D., Martin, L., & Msendema, B. (2020). The Digitalization of Routine Data Management Processes at the Point-Of- Care : The case of e-Tracker in Ghana The Digitalization of Routine Data Management Processes at the Point-Of-Care : The case of e-Tracker in Ghana. June 2021. https://doi.org/10.12856/JHIA-2020-v7-i2-285
Byrne, E., & Sæbø, J. I. (2022). Routine use of DHIS2 data: a scoping review. BMC Health Services Research, 22(1), 1–16. https://doi.org/10.1186/s12913-022-08598-8 DOI: https://doi.org/10.1186/s12913-022-08598-8
County Goverment Of Kiambu. (2023). Health services – Kiambu County Government.
County Goverment Of Kiambu. (2024). Demographic features – Kiambu County Government.
Das, S., Radovich, E., Karki, S., & S. (2023). European Journal of Public Health, Volume 33 Supplement 2, 2023. 33, 2023. DOI: https://doi.org/10.1093/eurpub/ckad160.869
Dehnavieh, R. (2018). The District Health Information System ( DHIS2 ): A literature review and meta-synthesis of its strengths and operational challenges based on the The District Health Information System ( DHIS2 ): A literature review and meta- synthesis of its strengths and operational challenges based on the experiences of 11 countries. June. https://doi.org/10.1177/1833358318777713 DOI: https://doi.org/10.1177/1833358318777713
Farnham, A., Loss, G., Lyatuu, I., Cossa, H., Kulinkina, A. V, & Winkler, M. S. (2023). A roadmap for using DHIS2 data to track progress in key health indicators in the Global South : experience from sub saharan Africa. BMC Public Health, 1–9. https://doi.org/10.1186/s12889-023-15979-z DOI: https://doi.org/10.1186/s12889-023-15979-z
Grif, T., & Pabon-rodriguez, F. (2025). Strengthening health information systems and inherent statistical outputs for improved malaria control and interventions in western Kenya. June. https://doi.org/10.3389/fepid.2025.1591261 DOI: https://doi.org/10.3389/fepid.2025.1591261
Hagel, C., Paton, C., Mbevi, G., & English, M. (2020). Data for tracking SDGs : challenges in capturing neonatal data from hospitals in Kenya. March. https://doi.org/10.1136/bmjgh-2019-002108 DOI: https://doi.org/10.1136/bmjgh-2019-002108
Kariuki, J. M., Manders, E., Richards, J., Oluoch, T., & Kimanga, D. (2016). Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya : An Interoperability field-test using OpenMRS and DHIS2. 7(2). https://doi.org/10.5210/ojphi.v8i2.6722 DOI: https://doi.org/10.5210/ojphi.v8i2.6722
Kenya National Bureau of Statistics & ICF. (2022). Kenya Demographic and Health Survey 2022: Key indicators report.
Lasim, O. U., Ansah, E. W., & Apaak, D. (2022). Maternal and child health data quality in health care facilities at the Cape Coast. BMC Health Services Research, 1–15. https://doi.org/10.1186/s12913-022-08449-6 DOI: https://doi.org/10.1186/s12913-022-08449-6
Manya, A., & Nielsen, P. (2017). Reporting Practices and Data Quality in Health Information Systems in Developing Countries : An Exploratory Case Study in Kenya Reporting Practices and Data Quality in Health Information Systems in Developing Countries : An Exploratory Case Study in Kenya. December 2016.
Meidani, Z., Zare, S., & Holl, F. (2022). Methods of Information in Medicine Development and testing requirements for an integrated maternal and child. June. https://doi.org/10.1055/a-1860-8618 DOI: https://doi.org/10.1055/a-1860-8618
Ministry of Health and Wellness Republic of Botswana. (2020). Botswana health data collaborative roadmap: Towards a harmonized health information and monitoring & evaluation system in Botswana (2020-2025).
Ministry of Health Republic of Kenya. (2020). National community health digitization strategy.
Moradzadeh, M., Karamouzian, M., Najafizadeh, S., & Yazdi-feyzabadi, V. (2023). Editorial International Journal of Health Policy and Management ( IJHPM ): A Decade of Advancing Knowledge and Influencing Global Health Policy ( 2013-2023 ). Kerman University of Medical Sciences, 12(April), 8124. https://doi.org/10.34172/ijhpm.2023.8124 DOI: https://doi.org/10.34172/ijhpm.2023.8124
Odeny, B. M., Njoroge, A., Gloyd, S., Hughes, J. P., Wagenaar, B. H., Odhiambo, J., Nyagah, L. M., Manya, A., Oghera, O. W., & Puttkammer, N. (2023). Development of novel composite data quality scores to evaluate facility level data quality in electronic data in Kenya : a nationwide retrospective cohort study. BMC Health Services Research, February 2024. https://doi.org/10.1186/s12913-023-10133-2 DOI: https://doi.org/10.1186/s12913-023-10133-2
Orhan, M. (2021). Systematic Review on Reengineering Digital Processes of Healthcare Institutions Sa ğ l ı k Kurumlar ı n ı n Dijital Süreçlerin Yeniden Yap ı land ı r ı lmas ı Üzerine Sistematik Derleme. 6(4). https://doi.org/10.5336/healthsci.2020-80883 DOI: https://doi.org/10.5336/healthsci.2020-80883
Oware, P. M., Omondi, G., Adipo, C., Adow, M., Aluvaala, J., Ngwatu, P., Githanga, D., Kinuthia, D., Amadi, I., Kahtra, A., Mulwa, A., Amoth, P., Agweyu, A., & Were, F. (2025). Perceived accuracy and utilisation of DHIS2 data for health decision making and advocacy in Kenya : A Qualitative Study. 1–22. https://doi.org/10.1371/journal.pgph.0004508 DOI: https://doi.org/10.1101/2025.03.25.25324643
Penumetsa, M., Neary, J., Farid, S., Kithao, P., Richardson, B. A., Matemo, D., John-stewart, G., Kinuthia, J., & Drake, L. (2020). Implementation of HIV Retesting During Pregnancy and Postpartum in Kenya : A Cross-Sectional Study. 10(1), 1–11. DOI: https://doi.org/10.9745/GHSP-D-21-00451
Rahman, A., Id, P., Kodela, S., Timsina, L., & Mathew-steiner, S. S. (2022). Development and validation of the DHIS2 platform for integrating sociomedical data to study wound care outcomes. 1–21. https://doi.org/10.1371/journal.pone.0308553 DOI: https://doi.org/10.1371/journal.pone.0308553
Rambo, D. A., Odila, P., Ndenga, A., Odipo, E., & Muhambe, C. (2024). Enhancing data accuracy and reliability in maternal and child health : MCGL success story. 36(1), 2024. DOI: https://doi.org/10.59692/jogeca.v36i1.282
Sylla, B., & Diallo, G. (2024). Improving Health Information Systems Data Quality in Sub-Saharan Africa. November. https://doi.org/10.3233/SHTI240428 DOI: https://doi.org/10.3233/SHTI240428
Teklegiorgis, K., Dawa, D., Health, R., Dawa, D., Gebremariam, K. T., & Sebagadis, G. M. (2016). Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors , eastern Ethiopia. August. https://doi.org/10.4102/sajim.v17i1.612 DOI: https://doi.org/10.4102/sajim.v17i1.612
United Nations Children’s Fund. (2020). Nutrition Information in Routine Reporting Systems: A Landscape Analysis for UNICEF’s Eastern and Southern Africa Region, UNICEF Eastern and Southern Africa Regional Office.
WHO. (2023). Trends in maternal mortality estimates 2000 to 2023, by WHO, UNICEF,World Banck Group and UNDESA.
Wittenauer, R., Dolan, S. B., Njoroge, A., Onyango, P., Owiso, G., Rabinowitz, P., & Puttkammer, N. (2023). Usability and Acceptability of Electronic Immunization Registry Data Entry Workflows From the Health Care Worker Perspective in Siaya , Kenya ( Part 3 ): Pre-Post Study Corresponding Author : Related Articles : 7, 1–12. https://doi.org/10.2196/39383 DOI: https://doi.org/10.2196/39383
Zerfu, T. A., Asressie, M., Tareke, A. A., Begna, Z., Habtamu, T., Werkneh, N., Nigatu, T., Jisso, M., & Genta, A. (2025). Contributions of District Health Information Software 2 ( DHIS2 ) to maternal and child health service performance in Ethiopia : an interrupted time series mixed methods study. 2(July). https://doi.org/10.1186/s13690-025-01641-0 DOI: https://doi.org/10.1186/s13690-025-01641-0
