Trusted but Not Fully Verified: Generative AI Use and Exposure to Errors in Clinical Documentation Among Healthcare Professionals

Background/Objectives: Generative artificial intelligence (GenAI) tools are entering clinical documentation ahead of institutional governance, and little is known about whether clinicians who rely on them also check their output, particularly in Central and Eastern Europe. This study characterized regular GenAI use for documentation, verification practices, and exposure to clinically relevant errors among healthcare professionals at a Romanian tertiary center, and identified the attitudes independently associated with regular use. Methods: In a cross-sectional survey of 126 physicians, residents, nurses, and public health specialists (46.0% of 315 invited), regular use was defined as at least weekly use for documentation during the preceding three months. Composites measured AI literacy, trust in output accuracy, perceived confidentiality risk, perceived usefulness, documentation burden, and intention; emotional exhaustion was measured with the Maslach Burnout Inventory. Correlates of regular use were examined with bootstrap-validated multivariable logistic regression, and differences in burden and exhaustion with propensity-score overlap weighting. Results: Regular use was reported by 56 participants (44.4%; 95% confidence interval [CI] 36.1–53.2), mostly with ChatGPT (89.3%) for referral and discharge letters (78.6%) and discharge summaries (69.6%). Users verified a mean of 70.3% (95% CI 65.6–75.0) of outputs line by line, and 36 (64.3%; 95% CI 51.2–75.5) had encountered at least one clinically relevant error. Among users, higher trust was associated with a lower proportion of outputs verified (Spearman ρ = −0.31; p = 0.020). Concern about factual errors was common in both groups (73.2% vs. 77.1%), whereas confidentiality concern, perceived insufficient training, and fear of liability were less frequent among users. Higher trust (adjusted odds ratio [OR] 2.36 per 10 points; 95% CI 1.34–4.15), higher perceived usefulness (OR 1.90; 1.03–3.50), and lower perceived risk (OR 0.49; 0.29–0.83) were independently associated with regular use (optimism-corrected area under the curve 0.905). After overlap weighting, differences in documentation burden (−4.8 points; 95% CI −12.1 to 2.6) and emotional exhaustion (−1.7; −5.9 to 2.5) were not statistically significant but were imprecisely estimated. Conclusions: Regular GenAI use coexisted with incomplete verification and frequent error exposure and was linked to trust and perceived usefulness; AI literacy and training were not independently associated with use once these attitudes were accounted for. Associations with documentation burden and emotional exhaustion were inconclusive. Institutions should treat verification as a distinct, trainable behavior and pair access with explicit confidentiality guidance.