An analytics framework for secure and intelligent telemedicine in sustainable healthcare

Telemedicine has significantly transformed healthcare delivery by expanding access to medical services and enabling more personalized care. Despite these advances, healthcare organizations continue to face challenges related to digital literacy, uneven access to technology, data privacy, platform interoperability, and the integration of advanced digital solutions into clinical and operational workflows. This paper proposes an analytics-driven telemedicine framework that emphasizes secure data governance, interoperability, and decision support in healthcare organizations. The framework integrates permissioned blockchain services to provide immutable logging, identity and access management, and end-to-end traceability of clinical and pharmaceutical activities, while federated learning supports decentralized model training without centralizing sensitive patient data. In addition, large language models are incorporated to enhance clinical text understanding and enable multimodal decision making. The framework is evaluated through controlled experiments across two complementary dimensions. An operational simulation of healthcare service indicators demonstrates meaningful performance gains under controlled conditions, reducing consultation response times from 24 to 72 h to 1 to 4 h, shortening diagnostic delays from 3 to 7 days to 2 to 12 h, and decreasing medication traceability errors from 2 to 8 percent annually to below 0.5 percent. A multimodal analytics assessment using 15,000 paired medical images and clinical reports related to breast, liver, and lung cancer shows strong predictive performance, with the best configuration achieving 93.5% accuracy, 94.5% precision, and 91.5% recall, along with low cross entropy values that indicate reliable classification and coherent report generation. Overall, the results indicate that the integration of advanced analytics with secure and auditable data infrastructure can improve efficiency, data integrity, and clinical decision support in telemedicine systems. The framework is particularly relevant for healthcare environments where institutional fragmentation and infrastructure limitations require scalable, privacy-preserving, and interoperable digital solutions.