AI-driven multimodal workflow optimization for personalized patient-centered care

This research presents a novel multimodal artificial intelligence framework designed to optimize healthcare workflows and enhance personalized patient-centered care. The approach integrates four critical data streams: Electronic Health Records, patient-reported outcomes, genomic and molecular data, and real-time physiological information from wearable sensors. Unlike traditional healthcare AI applications that operate in isolated data silos, our system creates a comprehensive patient profile that enables more holistic and personalized care decisions. Case studies in chronic disease management, perioperative care, and mental health interventions demonstrate significant improvements in clinical outcomes, patient satisfaction, and provider efficiency. The framework consists of five integrated layers: Data Acquisition, Preprocessing, Multimodal Integration, Personalization Engine, and Interactive Interface. Rather than replacing clinical judgment, the system augments decision-making by revealing insights that would remain hidden in fragmented data systems, allowing clinicians to spend less time on administrative tasks and more time on meaningful patient interactions. Despite promising results, challenges remain in technical integration, implementation, regulatory compliance, and scalability. Future directions include incorporating social determinants of health, developing advanced explainability tools, creating specialty-specific interfaces, exploring federated learning approaches, and quantifying long-term impacts on healthcare costs and outcomes.