HEALTH WORKER FACTORS INFLUENCING HEALTH INFORMATION UTILIZATION IN JUBA COUNTY, SOUTH SUDAN
Keywords:
health worker factors, health information utilization, HMIS, data quality, Juba County, South SudanAbstract
Purpose of Study: This study aimed to examine health worker factors influencing health information utilization in Juba County, South Sudan. It focused on determining how professional training, information management competence, technology skills, access to routine data, and perceived data quality influence the use of health information for decision-making within public health facilities.
Methodology: A quantitative descriptive cross-sectional research design was used among 220 health workers from 12 public health facilities in Juba County. Data were collected using structured self-administered questionnaires and analyzed using SPSS version 27. Descriptive statistics, chi-square tests, and Fisher’s exact tests were applied to determine associations between health worker factors and information utilization.
Findings: The study achieved a 100% response rate. Training in data utilization (p=0.013) and HMIS software (p=0.028) significantly influenced health information use. Competence in information management tasks and ease of accessing routine data were strongly associated with utilization (p=0.0001). All assessed data quality dimensions, including timeliness, accuracy, reliability, completeness, relevancy, and credibility, significantly predicted information use. Major barriers included lack of motivation and feedback (63.6%), multiple reporting levels (60.9%), excessive data demands, and inadequate training. Findings demonstrate that both individual capacity and organizational support are critical for effective health information utilization.
Conclusion: Health information utilization in Juba County depends on skilled health workers, reliable data systems, and supportive organizational practices. Strengthening targeted HMIS training, improving access to routine data, enhancing digital capacity, and establishing regular feedback mechanisms are essential strategies for promoting evidence-based decision-making and improving health system performance.
References
Addo, K., & Agyepong, P. K. (2024). Evaluating the health information system implementation and utilization in healthcare delivery. Health Informatics Journal, 30(1). https://doi.org/10.1177/14604582241304705
Brick, J. M., & Williams, D. (2013). Explaining rising nonresponse rates in cross-sectional surveys. The Annals of the American Academy of Political and Social Science, 645(1), 36–59. https://doi.org/10.1177/0002716212456834
Doka, B. K., Negeri, K. G., Worku, A. G., & Kassa, D. H. (2025). Effect of augmented capacity development interventions on information utilisation for decision-making in the routine health information system in public health institutions of Gofa Zone, southern Ethiopia: A cluster randomized controlled trial. Health Services Insights, 18. https://doi.org/10.1177/11786329251381429
Farnham, A., Matamoros-Caballero, E., Tatem, A. J., & Bhatt, 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, 23, 1030. https://doi.org/10.1186/s12889-023-15979-z
Kumasenu , N., Godwin Adzakpah, Kissi, J., Richard Okyere Boadu, Queensly Kyerewaa Acheampongmaa, Taylor-Abdulai, H., & Samuel Tamti Chatio. (2023). Perceived impact of digital health technology on health professionals and their work: A qualitative study in Southern Ghana. Digital Health, 9. https://doi.org/10.1177/20552076231218838
Ministry of Health, South Sudan. (2022). Health sector development plan monitoring and evaluation framework 2022–2026. Ministry of Health. https://moh.gov.ss/monitoring_and_evaluation.php
Mboera, L. E. G., Rumisha, S. F., Mbata, D., Mremi, I. R., Lyimo, E. P., & Joachim, C. (2021). Data utilisation and factors influencing the performance of the health management information system in Tanzania. BMC Health Services Research, 21(1). https://doi.org/10.1186/s12913-021-06559-1
Morris, L. D., Nyongesa, M. W., & Sokiri, T. D. (2024). Factors influencing data quality in routine health information systems in Maridi County, South Sudan. South African Journal of Information Management, 26(1), a1856. https://doi.org/10.4102/sajim.v26i1.1856
Owoyemi, A., Osuchukwu, J. I., Azubuike, C., Ikpe, R. K., Nwachukwu, B. C., Akinde, C. B., Biokoro, G. W., Ajose, A. B., Nwokoma, E. I., Mfon, N. E., Benson, T. O., Ehimare, A., Irowa-Omoregie, D., & Olaniran, S. (2022). Digital solutions for community and primary health workers: Lessons from implementations in Africa. Frontiers in Digital Health, 4, 876957. https://doi.org/10.3389/fdgth.2022.876957
Okeke, E. B., Forman, D., Taylor, E., Hyde, E., Parente, F., Kanmodi, K. K., & Wordsworth, S. (2025). Barriers and facilitators to digital health adoption: a thematic analysis of healthcare professionals’ perspectives in rural Oyo State, Nigeria. BMC Digital Health, 3(1). https://doi.org/10.1186/s44247-025-00227-8
Qian, J., Shiferaw, S., Seme, A., & Yirgu, R. (2023). Data for local decision-making, not a mere reporting requirement: Development of an index to measure facility-level use of HMIS data. Journal of Global Health Reports, 7, e2023011. https://doi.org/10.29392/001c.75141
Rendell, N., Lokuge, K., Rosewell, A., & Field, E. (2020). Factors That Influence Data Use to Improve Health Service Delivery in Low- and Middle-Income Countries. Global Health: Science and Practice, 8(3), 566–581. https://doi.org/10.9745/GHSP-D-19-00388
Rumisha, S. F., Lyimo, E. P., Mremi, I. R., Tungu, P. K., & Mboera, L. E. G. (2021). Data utilisation and factors influencing the performance of the health management information system in Tanzania. BMC Medical Informatics and Decision Making, 21, 129. https://doi.org/10.1186/s12911-021-01493-y
Tafere, N. G., Damtew, B., Aliye, A., Kuliche, A., & Tolera, M. (2026). Level and predictors of routine health management information system data management practice among health professionals working at public health facilities in Dire Dawa administration, eastern Ethiopia. BMC Health Services Research. https://doi.org/10.1186/s12913-026-14382-9
World Health Organization. (2023). Digital health literacy key to overcoming barriers for health workers. WHO/Europe. https://www.who.int/europe/news/item/18-09-2023-digital-health-literacy-key-to-overcoming-barriers-for-health-workers--who-study-says
Wang, W., Ferrari, D., Haddon-Hill, G., & Curcin, V. (2023). Electronic Health Records as Source of Research Data (O. Colliot, Ed.). PubMed; Humana. https://www.ncbi.nlm.nih.gov/books/NBK597466/
World Health Organization. (2024). Strengthening health information systems. WHO/Europe. https://www.who.int/europe/teams/data-and-digital-health/strengthening-health-information-systems
Zeng, V., Kankpetinge, C., Dongdem, A. Z., & Tabong, P. T.-N. (2025). Utilization of health management information systems for decision making among healthcare professionals in Ketu North Municipality, Ghana: A descriptive cross-sectional study. Pan African Medical Journal, 51, 66. https://doi.org/10.11604/pamj.2025.51.66.41385
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 James Lual Garang Diing, Lily Masinde

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.