The AI Use in Healthcare Anxiety Scale (AI-HAS) for Adult Patients: Development, Validity and Reliability

Background/Objectives: Artificial intelligence (AI) is increasingly used in healthcare, but concerns about diagnostic errors, data security, reduced human interaction, and opaque algorithmic decisions may cause patient anxiety and hinder acceptance. This study developed and psychometrically validated the Artificial Intelligence Use in Healthcare Anxiety Scale (AI-HAS) for adult outpatients. Methods: Following DeVellis’s eight-step framework, a three-member expert panel comprising two psychologists with PhDs in psychology and an expert in AI applications in education with a PhD in educational technology generated 35 items. Cognitive debriefing and two rounds of formal expert review reduced the pool to 25 items. Participants were recruited by convenience sampling at Kırıkkale University Faculty of Medicine Hospital, Türkiye. Exploratory factor analysis included 369 participants after excluding 13 multivariate outliers identified using Mahalanobis distance (p < 0.001). Confirmatory factor analysis used an independent sample of 225 participants. Test–retest reliability was assessed in 26 participants after approximately 30 days using Pearson’s correlation. Results: Exploratory factor analysis supported an 18-item, four-factor structure explaining 65.80% of the variance: Anxiety Regarding Negative Outcomes, Security Anxiety, AI Integration Anxiety, and Accuracy Anxiety. Confirmatory factor analysis yielded χ2(df, p) = 328.4 (128), p < 0.001; RMSEA = 0.082 [0.074, 0.097]; CFI = 0.930; TLI = 0.916; and SRMR = 0.056. Cronbach’s alpha and McDonald’s omega coefficients ranged from 0.83 to 0.91. Conclusions: The AI-HAS showed preliminary evidence of validity and reliability for measuring adult outpatients’ anxiety about AI use in healthcare. Further validation in more representative samples is warranted.