The rapid adoption of Large Language Models (LLMs) in healthcare has opened new opportunities for automating clinical documentation and optimizing workflow systems. Healthcare professionals face significant administrative burdens due to extensive documentation requirements, fragmented electronic health record (EHR) systems, and time-consuming manual data entry. LLMs, such as transformer-based architectures trained on large-scale biomedical and general corpora, offer capabilities in natural language understanding, summarization, and generation that can streamline clinical documentation processes
The rapid adoption of Large Language Models (LLMs) in healthcare has opened new opportunities for automating clinical documentation and optimizing workflow systems. Healthcare professionals face significant administrative burdens due to extensive documentation requirements, fragmented electronic health record (EHR) systems, and time-consuming manual data entry. LLMs, such as transformer-based architectures trained on large-scale biomedical and general corpora, offer capabilities in natural language understanding, summarization, and generation that can streamline clinical documentation processes
Despite these advantages, challenges such as data privacy, hallucination risks, model bias, regulatory compliance, and integration with legacy EHR systems remain critical concerns. The paper also discusses mitigation strategies including domain-specific fine-tuning, retrieval-augmented generation (RAG), and secure deployment frameworks. The study concludes that while LLMs are not replacements for clinicians, they serve as powerful assistive tools for intelligent healthcare documentation and workflow optimization
