Agentic Workflows in Healthcare: Advancing Clinical Efficiency through AI Integration

Abstract

This article explores the transformative impact of agentic workflows in healthcare settings, focusing on their implementation and effectiveness in addressing critical challenges in clinical operations. Agentic workflows, powered by advanced artificial intelligence technologies including domain-specific Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, represent a paradigm shift from traditional automation approaches. These intelligent systems demonstrate sophisticated capabilities in managing complex healthcare tasks, from clinical documentation to patient management. It examines the integration of these technologies across various healthcare domains, evaluating their performance through both technical metrics and clinical impact assessments. The article highlights significant improvements in operational efficiency, clinical decision support, and patient care delivery through the implementation of these advanced systems. Furthermore, it discusses future directions in healthcare AI, including enhanced subspecialty models, advanced natural language processing capabilities, and improved predictive analytics for population health management, providing a comprehensive overview of the evolving landscape of healthcare automation.