Current Status and Determinant Factors of Telemedicine Adoption in Selected Commonwealth of Independent States (CIS) Countries; A Mix Method Study - Mahir Alisa, Safarov; Aytan Yagub, Mammadzada; Shahzada Musa, Polukhova; Zumrud Amirgulu, Abaszade; Shalala Garib, Ismayilova; Fuad Yusir, Mammadov; Parvana Akbar, Mahmudova (Archives of Academic Emergency Medicine, 2026)
Introduction: The field of telemedicine has become an urgent innovation in the healthcare field worldwide, but there is still an unequal distribution of its implementation in transitional economies. This study aimed to evaluate the associated factors of telemedicine adoption in selected countries of the commonwealth of independent states (CIS) region. Methods: A validated survey was used to collect data on 600 healthcare professionals, patients, information technology (IT) specialists and policymakers from selected CIS countries, using a mixed-method design. Through a designed and validated questionnaire, solid statistical techniques, and cross-regional analyses, the barriers and facilitators of telemedicine adoption in studied countries were evaluated. Results: The general average score of telemedicine adoption was 3.84 ± 0.92. The highest mean adoption score was observed in Azerbaijan (4.02 ± 0.85), Russia (3.91 ± 0.88) and Ukraine (3.87 ± 0.91). There were significant differences between regions regarding mean adoption score (p < 0.001). Clinician acceptance (r = 0.64; p < 0.01), infrastructure readiness (r = 0.58; p < 0.01), regulatory maturity (r = 0.42; p < 0.01), and patient digital literacy (r = 0.36; p < 0.01) had the strongest correlation with telemedicine adoption. The most predictive factors of telemedicine adoption were infrastructure readiness (β (standard error; SE) = 0.42 (0.05), p < 0.001), then clinician acceptance (β (SE) = 0.39 (0.06), p < 0.001), patient digital literacy (β (SE) = 0.22 (0.05), p < 0.001), and regulatory maturity (β (SE) = 0.18 (0.04), p < 0.001). Professional experience had a minor yet significant impact (β = 0.09, t = 0.038). Logistic regression showed increased infrastructure readiness score (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 1.21-1.81), clinician acceptance score (OR = 1.56, 95% CI = 1.28-1.92), regulatory maturity score (OR = 1.31, 95% CI = 1.09-1.58), and patient literacy score (OR = 1.22, 95% CI = 1.03-1.45) as the predictors of high telemedicine adoption (≥70%). The model accurately categorized 78.2% of data and the area under the curve 0.79 (95% CI: 0.75–0.83) meaning the model is a strong predictor. Conclusion: The findings showed that the structural investments cannot be made alone without the involvement of professionals. The research contributes to the existing body of transitional economies research by offering strong comparative evidence of telemedicine and provides policy recommendations on how to improve infrastructure, generate harmonization, and capacity building of clinicians and patients to support sustainable digital health ecosystems.