Factors influencing data quality in routine health information systems in Maridi county, South Sudan

Abstract

Background: Health system planning and monitoring rely on routine data collection, analysis and utilisation. However, underdeveloped countries need more data for decision-making. South Sudan’s data management framework only partially functions, with delayed data collection and inaccurate data. The study examined the factors affecting data quality in Maridi County, South Sudan, aiming to improve resource forecasting and equitable health service delivery. Objective: The study sought to identify the obstacles and opportunities for improving data quality in health information systems (HIS) in Maridi County, Western Equatoria State, South Sudan. Methods: A cross-sectional study involving 106 respondents was conducted on 12 healthcare facilities in Maridi County. Statistical Package for the Social Sciences (SPSS) version 25 was used for descriptive, factor and thematic analysis to understand data quality, focussing on behavioural, organisational and technical aspects. Result: The study revealed that insufficient motivation, negative staff attitudes, excessive workloads, a lack of cooperation, personnel insufficiency, inadequate supervision, feedback and training influenced data quality. These factors were interrelated, with over 50% of variables showing weak to strong correlations. Set of standard indicators correlated with the presence of standard data collection tools (r = 0.51). Regular feedback from the County Health Department linked with completeness (r = 0.63) and the training of personnel on health management information systems (HMIS) and completeness resulted in moderate association (r = 0.488). Conclusion: Staff motivation, optimal staffing, training, regular feedback, and continuous supervision are crucial for maintaining the appropriate skill set for data quality. Contribution: Data quality can be achieved when standard tools and human resources are maintained and are evenly distributed.

Keywords

• data quality
• routine health information system
• health facilities
• behavioral factors
• technical factors
• organizational factors.