Healthcare providers delivering in-home urgent care must make timely, accurate decisions using patient information that is often fragmented across electronic health records, clinical notes, referral documents, operational systems, and real-time field data. Converting this scattered mix of structured and unstructured information into trusted, actionable decisions is critical for improving patient outcomes while maximizing limited clinical resources.
We will present a case-study about a next-generation, AI-powered platform that transforms mobile integrated healthcare by unifying an intelligent human in the loop triage agent, AI-generated clinical notes, accurate volume forecasting, and an automated service-assignment agent. The Triage Agent assists clinicians during patient intake by transcribing phone calls, extracting key information from those calls, analyzing symptoms, patient history, and clinical guidelines to recommend the appropriate level of care with transparent, explainable recommendations. The human in the loop reduces patient care risks and provides the feedback to improve agent’s performance. The Service Provisioning Agent then identifies the most appropriate paramedic based on clinical qualifications, availability, location, workload, and equipment, generates an optimized route, and continuously adapts assignments as conditions such as traffic, emergencies, or resource availability change.
Attendees will see how the solution enables organizations to build trusted and highly accurate AI systems that combine traditional AI, semantic graph intelligence, business rules, and agentic workflows to turn scattered healthcare data into real-time, actionable intelligence, delivering faster response times, improved operational efficiency, and better patient care.
