Applying the UTAUT Model to Analyze Healthcare Professionals’ Behavioural Intention to Use Hospital Information Systems: A Cross-Sectional Study in a Multi-Specialty Hospital

Background/Objectives: Although Hospital Information Systems (HIS) are essential to the provision of contemporary healthcare, clinical professionals’ use of HIS is still uneven. Robust healthcare decision-making is based on the systematic collection, storage, and analysis of health data, and it is crucial to comprehend the elements that promote or impede adoption. In a tertiary-care multi-specialty hospital in Chennai, India, this study sought to evaluate the role of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on the Behavioural Intention of healthcare professionals to adopt HIS. Methods: 140 medical professionals (physicians, nurses, and hospital technicians) from a 750-bed teaching hospital where HIS had been in use for at least 24 months were chosen by stratified random sampling to participate in a descriptive, cross-sectional study. The original UTAUT instrument was modified into a structured, self-administered questionnaire using a validated 5-point Likert scale. Expert review was used to demonstrate face validity, while Cronbach’s Alpha (α > 0.70) was carried out. Statistical analysis methods included Pearson correlation, multiple linear regression, one-way ANOVA with Tukey’s HSD post hoc analysis, and Structural Equation Modelling (SEM). Results: The majority of responders in the sample were female (51.5%), primarily nurses (47%), and had less than five years of work experience (36%). All four UTAUT constructs were found to be significantly correlated with Behavioural Intention by Pearson correlation, with Performance Expectancy showing the strongest association. The structural model explained a significant proportion of the variance in technology adoption. Multiple regression analysis indicated that Performance Expectancy (β = 0.480, p < 0.01) and Social Influence (β = 0.180, p < 0.05) were significant positive predictors of Behavioural Intention. Confirmatory Factor Analysis verified acceptable measurement boundaries (χ2/df = 1.42, RMSEA = 0.043, SRMR = 0.062, CFI = 0.94. An exploratory one-way ANOVA revealed that perceptions of Facilitating Conditions differed significantly by professional designation (F (2, 137) = 6.42, p = 0.002), with nurses scoring significantly lower than physicians (p = 0.002) and technicians (p = 0.011). Conclusions: Performance Expectancy is the main driver of healthcare professionals’ Behavioural Intention to adopt HIS. Compared to doctors and technical professionals, nurses reported considerably lower perceptions of Facilitating Conditions, indicating a substantial support gap. In order to close the clinical digital gap and enhance patient safety, these findings advocate for role-specific infrastructure investments and focused implementation techniques.