Determinants of Health Information Technology Readiness among Patients with Type 2 Diabetes: Implications for AI-Driven Solutions and Patient Characteristics - Kahouei, Mehdi; Paknazar , Fatemeh; Pahlevanynejad , Shahrbanoo; Kermani , Farzaneh; Foroutan, Majid; Mehdizadeh , Jamileh; Doostmohammadi , Sanaz; Poshtvan, Marzeih (Journal of Liaquat University of Medical & Health Sciences, 2026)
OBJECTIVE: To investigate the influence of specific patient characteristics, including employment status, comorbidities, disease duration, and medication knowledge, on patients' readiness to adopt Health Information Technology (HIT) to inform the development of effective, AI-driven solutions. METHODOLOGY: This cross-sectional study involved 300 patients with type 2 diabetes (T2DM) from healthcare centres affiliated with Semnan University of Medical Sciences between February and December 2024. Eligible participants were smartphone owners aged 18–65 without severe conditions. The sample size was calculated based on the Events Per Variable (EPV) rule for logistic regression. Readiness for HIT was assessed using the Persian version of Koopman's HIT Readiness Scale. The outcome variable was dichotomized into 'High' and 'Low' readiness, and the data were analyzed using descriptive statistics, bivariate tests, and binary logistic regression. RESULTS: The study found significant relationships between HIT readiness and employment status (Adjusted OR=1.82, 95%CI 1.108-2.99, P=0.018), comorbidities (Adjusted OR=0.56, 95%CI 0.336-0.94, P=0.028), duration of diabetes (Adjusted OR=0.45, 95%CI 0.268-0.743, P=0.002), and knowledge of diabetes medications (Adjusted OR=3.02, 95%CI 1.595-5.7, P<0.001). CONCLUSION: Specific patient characteristics, particularly socioeconomic status and clinical burden, significantly determine HIT readiness. Socioeconomic gaps reduce HIT readiness in vulnerable diabetic groups. These findings suggest that AI solutions, including voice assistants and predictive tools, could deliver customized, culturally sensitive support if tailored to these determinants. Addressing these barriers can reduce cognitive strain, improve healthcare access, prevent complications, and enable patients, thereby promoting sustainable, individualized diabetes care.