  Purpose: Large Language Models (LLMs) hold significant promise for medical
applications. Retrieval Augmented Generation (RAG) emerges as a promising
approach for customizing domain knowledge in LLMs. This case study presents the
development and evaluation of an LLM-RAG pipeline tailored for healthcare,
focusing specifically on preoperative medicine.
  Methods: We developed an LLM-RAG model using 35 preoperative guidelines and
tested it against human-generated responses, with a total of 1260 responses
evaluated. The RAG process involved converting clinical documents into text
using Python-based frameworks like LangChain and Llamaindex, and processing
these texts into chunks for embedding and retrieval. Vector storage techniques
and selected embedding models to optimize data retrieval, using Pinecone for
vector storage with a dimensionality of 1536 and cosine similarity for loss
metrics. Human-generated answers, provided by junior doctors, were used as a
comparison.
  Results: The LLM-RAG model generated answers within an average of 15-20
seconds, significantly faster than the 10 minutes typically required by humans.
Among the basic LLMs, GPT4.0 exhibited the best accuracy of 80.1%. This
accuracy was further increased to 91.4% when the model was enhanced with RAG.
Compared to the human-generated instructions, which had an accuracy of 86.3%,
the performance of the GPT4.0 RAG model demonstrated non-inferiority (p=0.610).
  Conclusions: In this case study, we demonstrated a LLM-RAG model for
healthcare implementation. The pipeline shows the advantages of grounded
knowledge, upgradability, and scalability as important aspects of healthcare
LLM deployment.
