Data-Driven Analysis of Digital Marketing Strategies for Predicting Telemedicine Adoption Among Underserved Populations

Telemedicine has expanded rapidly across the United States; however, adoption disparities persist among underserved populations, including low-income households, rural communities, minority groups, and elderly individuals. While infrastructure availability has improved, limited attention has been given to the role of digital marketing strategies in influencing equitable telemedicine uptake. This study investigates the impact of digital marketing strategies on telemedicine adoption among underserved populations using a hybrid analytical framework that integrates structural equation modeling and machine learning prediction techniques. A cross-sectional survey was conducted with 524 respondents across five U.S. regions identified as medically underserved. The study examines the effects of social media engagement, targeted digital advertising, localized content strategies, and mobile optimization on telemedicine adoption intention and actual usage behavior. Structural equation modeling results indicate that localized content and trust-building digital communication significantly influence adoption intention, while digital literacy moderates the relationship between marketing exposure and usage behavior. To enhance predictive robustness, logistic regression and random forest models were implemented to classify telemedicine adoption status, achieving classification accuracies of 74.2 percent and 81.6 percent, respectively. Feature importance analysis identifies trust, perceived usefulness, and mobile accessibility as the strongest predictors. The findings contribute to digital health equity research by integrating marketing analytics into telemedicine adoption modeling and provide actionable strategies for healthcare providers and policymakers aiming to reduce healthcare access disparities.