Technical factors influencing the use of data for evidence-based decision making amongst health workers at Kisumu County, Kenya


  • Morike Tom Department of Community Health and Epidemiology (KU), Nairobi, Kenya
  • Isaac Mwanzo Department of Community Health and Epidemiology (KU), Nairobi, Kenya
  • George Otieno Department of Health Management and Informatics, Nairobi, Kenya
  • Peter Kamau School of Public Health (JKUAT), Nairobi, Kenya



Health facilities, Healthcare Workers, Data Users, Data Entry, Routine Data


Background: Health information system is a system that integrates data collection, processing, reporting, and use to influence policy-making, program action, and research, but 43% lack data analysis and interpretation skills and 42% use data to influence budget preparation.

Methods: Analytical cross-sectional design was used to study 205 HCWs in selected health facilities. Data was collected using a researcher-administered structured questionnaire and Key Informant Interview. Quantitative data analysis was conducted using SPSS version 26.0 and involved univariate and bivariate analysis. Chi-square were used to test the significance of the association between the dependent and independent variables (p<0.05). Qualitative data was analyzed by thematic content analysis.

Results: Over a third of respondents 77 (37.6%) rarely used routine data for decision making. Additionally, 66 (32.2%) and 62 (30.2%) sometimes and always use the routine data/health information generated for decision making. The results indicate statistically significant association between extent of training on data utilization (ꭓ2=8.690, df=2, p=0.008), overall levels of competency (ꭓ2=14.340; df 3; p=0.026) and access to routine data (ꭓ2=11.823; df 1; p=0.003) with the use of routine data for decision making.

Conclusions: Healthcare workers use routine health information for decision making, but information culture is not yet achieved due to decisions based on health needs, cost, personal liking and superiors' directives. To create organizational culture, hospital management, donors and other stakeholders should provide continuous training to health workers with specific focus on use of routine health information.

Author Biography

Morike Tom, Department of Community Health and Epidemiology (KU), Nairobi, Kenya




Tabesh N. From data to decision: An implementation model for the use of evidence-based medicine, data analytics, and education in transfusion medicine practice. ProQuest Diss Theses. 2015;47(12):289-97.

Health Information Systems. Toolkit on monitoring health systems strengthening. World Heal Organ Libr. 2015;46(6):11-6.

Asiimwe AK. Determinants of Effective Utilization of Routine Health Information within Private Facilities in Kampala-Uganda. BMC Public Health. 2016;110(2): 1110-8. d

Whitaker D. The Use of Evidence-Based Design in Hospital Renovation Projects. J Soc Sci Res. 2018; 77(6):1142-6.

Mutemwa R. HMIS and decision-making in Zambia: Re-thinking information solutions for district health management in decentralized health systems. Health Policy Plan. 2016;21(1):40-52.

Scientific Symposium Report. Data Driven Decision Making to Control the HIV Epidemic moving to and beyond 2020. Mesh Consort. 2020;20(3):31-9.

Ministry of Health. Guidelines for Evidence Use in Policy-Making. USAID. 2020;45(5):100-11.

Chikanda A. Skilled health professionals’ migration and its impact on health delivery in Zimbabwe. J Ethn Migr Stud. 2016;32(4):667-80.

Yarinbab TE, Assefa MK. Utilization of HMIS Data and Its Determinants at Health Facilities in East Wollega Zone , Oromia Regional State , Ethiopia : A Health Facility Based Cross-Sectional Study. J Med Heal Sci. 2018;7(1):4-9.

Doolan-grimes M. Evidence-Based Practice from the perspectives of Mid-level and Frontline Nurse Managers A Qualitative Descriptive Study. Health Aff. 2014;36(1):99-106.

Nicol E, Bradshaw D, Uwimana-Nicol J, Dudley L. Perceptions about data-informed decisions: An assessment of information-use in high HIV-prevalence settings in South Africa. BMC Health Serv Res. 2017; 17(2):65-76.

Hardee K, Johnston A, Salentine S, et al. A Conceptual Framework for Data Demand and Information Use in the Health Sector. Int J Intell Inf Syst. 2015;161(28): 10-8.

Shaw C. How can hospital performance be measured and monitored? Heal Evid Netw Rep. 2014;3(1):1-6.

Cheburet SK, Odhiambo-Otieno GW. Process factors influencing data quality of routine health management information system: Case of Uasin Gishu County referral Hospital, Kenya. Int Res J Public Environ Heal. 2016;131(6):132-9.

Munda ME, Odhiambo-Otieno G, Wambui Kaburi L, Kainyu Kinyamu R. Routine Health Management Information Use in the Public Health Sector in Tharaka Nithi County, Kenya. Imp J Interdiscip Res. 2016;2(3):2454.

Teklegiorgis K. Factors associated with low level of health information utilization in resources limited setting, Eastern Ethiopia. Int J Intell Inf Syst. 2014;3(6):69-77.




How to Cite

Tom, M., Mwanzo, I., Otieno, G., & Kamau, P. (2023). Technical factors influencing the use of data for evidence-based decision making amongst health workers at Kisumu County, Kenya. International Journal Of Community Medicine And Public Health, 10(4), 1362–1368.



Original Research Articles