Transforming Clinical Workflows: From Reactive Systems to Proactive Care

Traditional clinical workflows are reactive , static and rule-based. They are most likely to fail when dynamicity of the patients’ condition such as comorbidities, acute events, or therapy responses needs to be taken into account. Recent advances in AI,  have expanded the scope of automation and data-driven insights in healthcare. In this context, Agenti Artificial Intelligence and Large Language Models play a crucial role. Unlike traditional rule-based systems, which operate on predefined triggers, Agentic AI and LLMs represents a new paradigm that can dynamically respond to real-time events within the clinical environment. This research work explores gaps in the current system and how it can be addressed using Agentic AI and LLMs.