Factors Influencing Data Quality in Routine Health Information System at Health Facilities in Maridi County- South Sudan

Abstract

Background: The study examined the factors affecting data quality in Maridi County, South Sudan, aiming to improve resource forecasting and equitable health service delivery. The lack of data has led to drug shortages and late reporting of morbidity data, causing the Ministry of Health to use a push system for resource allocation. This system is problematic, as many health facilities struggle to meet set targets. Methods: A descriptive cross-sectional study was conducted on 12 functional healthcare facilities in Maridi County, with 106 respondents selected using simple random sampling. The researcher used SPSS version 25 for descriptive analysis, factor analysis to understand the relationship between independent and dependent variables, and thematic analysis to generate critical perspectives on data quality, focusing on behavioral, organizational, and technical aspects. Results: The study found that data quality is impacted by insufficient motivation, negative staff attitudes, excessive workloads, lack of cooperation, personnel insufficiency, inadequate supervision, feedback, and training, with over 50% of variables showing weak to strong correlations in the factor analysis results. The regular feedback from the CHD (r=0.683, p=0.007<0.05), review meetings on data quality (r=0.522, p=0.041<0.05) & years of work experiences (r=-0.555, p=0.031<0.05) found to have a significantly strong correlation with data quality. In contrast, the other variables have an insignificant correlation with data quality.

Conclusion: The study reveals challenges in data quality, such as lack of motivation, work overload due to inadequate human resources, poor supervision of health facilities, feedback, insufficient training, and lack of reporting tools. It suggests that several strategies can be used to achieve high-quality data, including staff motivation, hiring more health workers to fill human resource gaps, frequent facility supervision, feedback provision, staff training on HMIS, and provision of data collection and reporting tools by the County Health Department.