Factors Associated with Digital Health Information-Seeking Behavior Among Healthcare Professionals in Izmir: A Cross-Sectional Study

Background: Recently, artificial intelligence-based tools such as large language models (LLMs) have further transformed health information-seeking practices. This study aimed to assess digital health information-seeking behaviors among healthcare professionals working in İzmir, Türkiye, and to examine individual and environmental factors associated with these behaviors. Methods: A cross-sectional study was conducted among 380 healthcare professionals in İzmir. Data were collected using an online questionnaire comprising sociodemographic and health information-seeking items and the eight-item e-health literacy scale (eHEALS). Descriptive statistics and non-parametric tests were used for unadjusted comparisons. Multivariable binary logistic regression was performed to identify factors independently associated with LLM use, while multivariable linear regression with HC3-robust standard errors was used to identify factors independently associated with e-health literacy. To reduce sparse-data instability, conceptually compatible small categories were collapsed before refitting the multivariable models. Statistical significance was set at p < 0.05. Results: Overall, 73.2% of participants reported using LLMs (e.g., ChatGPT) for health-related information seeking. After multivariable adjustment, daily internet use of 3–6 h was associated with higher odds of LLM use compared with ≤3 h/day (adjusted OR = 2.0437, 95% CI: 1.1467–3.6423, p = 0.0153). E-health literacy was not independently associated with LLM use (adjusted OR = 0.9875, 95% CI: 0.9599–1.0158, p = 0.3825). In the e-health literacy model, the combined divorced/widowed group had lower adjusted scores than married participants (B = −4.4087, 95% CI: −7.4003 to −1.4171, p = 0.0039). Uncertainty about institutional scientific database access was also associated with lower e-health literacy (B = −6.1805, 95% CI: −8.9255 to −3.4356, p < 0.0001), whereas often/always reading online health information was associated with higher scores compared with never/rarely reading it (B = 2.6264, 95% CI: 0.5112–4.7416, p = 0.0149). Conclusion: LLM use was common among healthcare professionals, but it was not independently associated with e-health literacy. Patterns of internet use, marital status, institutional database awareness, and frequency of reading online health information showed independent associations with the study outcomes. These findings support targeted digital health and AI-literacy initiatives.