Exploring the Potential of Large Language Models in Addressing Challenges of Hospital Information Systems

Abstract

This study explores the potential of Large Language Models (LLMs) in addressing challenges within Hospital Information Systems (HIS). Through literature review, interviews with healthcare professionals, and proof-of-concept testing, the research identifies key pain points in current HIS and evaluates LLMs' capabilities in areas such as data summarization, medical coding, error prevention, and interoperability. The study demonstrates LLMs' promising applications in enhancing data retrieval, improving coding accuracy, and facilitating standardized data exchange. However, limitations in consistency, knowledge updating, and data privacy are noted. Recommendations for healthcare organizations and developers are provided, emphasizing the need for continued research and development to fully realize LLMs' potential in improving healthcare delivery and patient outcomes.