Why healthcare LLMs should address clinical quality measures

AI and large language models (LLMs) in healthcare can improve delivery and management but face challenges in data privacy, security, bias, regulations, and precision. AI can help clinicians by creating textual content, interpreting patient's speech into clinical concepts relevant to possible diagnoses. However, AI struggles with integrating factors like gender and race, leading to biases and data quality issues. The complexity of medical language also causes difficulties for AI. Clinical relevance engine can filter relevant patient information, aiding care quality and compliance. Natural language processing (NLP) can convert LLM generated texts into codes, reducing documentation burden, then is processed using an expert system for actionable, clinically relevant information.

Opens on Healthcare IT News