Multidimensional Digital Competence and the Quality of Routine Health Information among Health Workers in Bayelsa State

As health information systems become increasingly digital, the need for health workers to possess strong digital skills has grown. However, there is limited understanding of how different aspects of digital competence relate to the quality of routine health data particularly in resource-constrained settings like Nigeria. This study examined the link between multidimensional digital competence and the quality of routine health information among health workers in Bayelsa State, Nigeria. A cross-sectional survey was conducted across four public health facilities in the state, targeting staff involved in routine health data tasks. Based on the Taro Yamane formula, 242 participants were initially required from a population of 612, but after multistage stratified and simple random sampling, 200 usable responses were analyzed. Data were collected using a structured questionnaire and processed with SPSS version 26. Statistical methods included descriptive analysis, correlation tests, linear and multiple regression, one-way ANOVA, and independent-sample t-tests, with significance set at p < 0.05. Respondents reported high levels of digital competence (M = 3.45, SD = 0.42), perceived reporting accuracy (M = 3.95, SD = 0.54), and timeliness (M = 3.97, SD = 0.51). Positive correlations emerged between digital competence and both accuracy (r = 0.411, p < 0.001) and timeliness (r = 0.430). The model showed that digital competence accounted for 16.9% of the variation in accuracy and 18.5% in timeliness. In regression analysis, cognitive/informatics competence had the strongest influence on reporting performance, followed by technical/operational and privacy/security competencies. Differences in digital competence were significant across facilities, F(3,196) = 3.290, but not by gender, t(198) = 0.159. Digital competence is significantly associated with the quality of routine health information, though it explains less than a quarter of the variability in reporting outcomes. Cognitive and informatics skills appear to be the most influential component. The findings underscore the importance of strengthening health workers’ abilities in data processing, management, and advanced use of health software, as well as addressing disparities between facilities. However, due to the cross-sectional design, causal conclusions cannot be drawn about the relationship between digital skills and data quality.