From Conversation to Standardized Terminology: An LLM-RAG Approach for Automated Health Problem Identification in Home Healthcare.

ABSTRACT:

Background

With ambient listening systems increasingly adopted in healthcare, analyzing clinician-patient conversations has become essential. The Omaha System is a standardized terminology for documenting patient care, classifying health problems into four domains across 42 problems and 377 signs/symptoms. Manually identifying and mapping these problems is time-consuming and labor-intensive. This study aims to automate health problem identification from clinician-patient conversations using large language models (LLMs) with retrieval-augmented generation (RAG).

Methods

Using the Omaha System framework, we analyzed 5118 utterances from 22 clinician-patient encounters in home healthcare. RAG-enhanced LLMs detected health problems and mapped them to Omaha System terminology. We

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12849705 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature