Empowering educators: AI literacy as a catalyst for competency-based health information training

Artificial intelligence (AI) transforms healthcare data collection, analysis, and application, making AI proficiency a growing necessity across health professions.ObjectiveThis study aimed to examine the influence of demographic factors on AI literacy among Health Information (HI) professionals, identify key knowledge gaps and inform workforce-aligned training recommendations.

This mixed-methods study analysed convenience-sampled survey data on AI literacy among HI professionals. Quantitative responses were examined with descriptive statistics, t-tests, Analysis of Variance (ANOVA), Spearman rank-order correlation, linear regression, geospatial analysis and a random forest to examine AI knowledge across demographic groups. The one qualitative open-ended response was analysed with latent Dirichlet allocation (LDA) topic modelling to identify themes.ResultsA total of 128 valid responses were analysed, including 22 participants who completed the technical knowledge section and 48 who responded to the open-ended question on AI education. Higher educational attainment and geographic location significantly predicted greater general AI literacy. However, no significant associations were found between AI literacy (general or technical) and age group, possession of non-health informatics credentials or prior AI experience. The cross-validated Random Forest models were assessed with and without oversampling. Accuracy was identical across both models (0.95), indicating that the overall prediction correctness of low versus high AI literacy was not affected by oversampling. The oversampled model had a superior ability to detect the minority class, making it more suitable for imbalanced classification tasks where recall is critical.

This study identified several important knowledge gaps on the influence of demographic factors on AI literacy, which informs workforce-aligned training recommendations. These findings underscore the need for competency-based education to strengthen AI readiness within the health information workplace.Implications for health information management practice:The thematic analysis demonstrated the urgent need for AI knowledge, training and literacy for HI professionals and students. Themes from the LDA topic modelling informed the development of AI educational frameworks, structured into domains, subdomains and specific components of educational competencies. With multidisciplinary collaboration and further research, standardised AI core competencies for HI professionals could be created, validated by experts and adopted across educational programs to improve AI literacy in the HI field.