Design and Development of a Multimodal Healthcare Platform for Patient Management

Healthcare today is evolving rapidly with the integration of digital technologies, artificial intelligence (AI), and multimodal systems that enable efficient patient data management and decision-making. However, conventional healthcare systems continue to face issues such as fragmented data, limited interoperability, and lack of real-time analytics. This research paper presents the design and development of a Multimodal Healthcare Platform (MHP) that bridges these gaps by integrating patient data from multiple sources—including medical reports, wearable sensors, doctor consultations, and diagnostic records—into one secure, intelligent system. The proposed platform incorporates AI-based predictive analytics, secure cloud storage, and interactive dashboards for patients, doctors, and administrators. It ensures privacy, scalability, and adaptability across medical institutions. The results demonstrate improved healthcare accessibility, faster decision-making, and reduced redundancy in medical record management. The paper also discusses ethical and data governance concerns, emphasizing security and transparency as the foundation of the proposed solution.