5th Edition of World Nursing Research Conference 2026

Abstract

The rapid adoption of telehealth across the Asia-Pacific region has transformed healthcare delivery, improving access to care while raising critical concerns regarding patient data security, privacy, and clinical safety. As telehealth systems increasingly support nursing practice and remote patient monitoring, safeguarding sensitive health information from cyber threats becomes essential to maintaining trust and quality of care. However, existing intrusion detection mechanisms often lack the adaptability and contextual awareness required for dynamic, resource-constrained healthcare environments.
This study proposes an ethical artificial intelligence–driven ensemble deep learning framework to strengthen cybersecurity in telehealth systems with direct implications for nursing practice. The framework integrates Long Short-Term Memory networks, Convolutional Neural Networks, and Deep Belief Networks to detect anomalous network activities and potential cyber intrusions affecting clinical data flows. Using the UNSW-NB15 dataset to simulate realistic healthcare-related attack scenarios, the model demonstrates improved detection performance, achieving 79% accuracy and reduced false positive rates compared to standalone models.
Beyond technical performance, the framework is designed with a strong emphasis on ethical AI principles, including data privacy, transparency, and reliability, which are critical in patient-centered care. By enhancing the security of telehealth platforms, this approach supports nurses and healthcare professionals in delivering safe, uninterrupted, and trustworthy remote care services. The findings highlight the importance of integrating intelligent cybersecurity solutions into digital health systems to promote patient safety, protect confidential health information, and strengthen resilience in modern nursing practice.
Keywords: Telehealth, patient safety, nursing informatics, cybersecurity, ethical AI, intrusion detection, digital health systems