Semester of Graduation
Fall 2025
Degree Type
Dissertation/Thesis
Degree Name
Master of Science in Information Technology
Department
College of Computing and Software Engineering
Committee Chair/First Advisor
Dr. Nazmus Sakib
Second Advisor
Dr. Liang Zhao
Third Advisor
Dr. Chloe Yixin Xie
Abstract
The digital healthcare field is expanding fast, and now it requires platforms that use advanced technology and maintain robust data security and compliance practices. In the present paper, we present the main structure, key methods, and compliance strategies of the digital healthcare system iHelpCare, which, while fully meeting the HIPAA/GDPR requirements, provides health services more accessible, efficient, and inclusive. The proposed platform is powered by AI for personalized care solutions, with the main emphasis on preventive health management and providing tools for people with disabilities.
iHelpCare achieves real-time patient monitoring while securing medical data management and easy communication between patients, caregivers, and healthcare providers. In addition, further consideration has been provided to the patients with Alzheimer's through memory aids, cognitive exercises for the patients, resources for the caregiver, and AI-enabled detection analytics. These features help in the early detection of cognitive decline, thus allowing timely interventions and an improved quality of life for such patients.
iHelpCare also addresses specific health needs in this community in general through the dissemination of culturally competent health materials, providing telehealth consultations in a number of languages, and offering connections to community support networks. The end result is an increase in improved healthcare access and efficiency for these patients. This platform uses AI tools in helping doctors with patient outcome predictions. Data helps them detect possible problems early, improve treatment options, and lighten the load on caregivers. Voice commands, screen readers, and gesture controls make the system usable by many more people since it is easy to be operated by users with physical or cognitive challenges. This includes better sensor integrations, personalized and inclusive models of care, improved security features, smart home supports, and clinically validated tools for both patients and their caregivers.
Included in
Information Security Commons, Other Computer Engineering Commons, Software Engineering Commons