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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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A Large Language Model Assistant for Summarizing Hepatology Referral Documents.

Hersh Shroff¹, Anubhav Shankar², Alison Baron¹

¹Division of Gastroenterology and Hepatology, University of North Carolina, Chapel Hill, North Carolina, USA.

The American Journal of Gastroenterology

|January 8, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) can create accurate patient record summaries for hepatology referrals, significantly reducing clinician triage time by 60%. This AI-powered approach streamlines workflows and maintains high accuracy.

Keywords:

burnout clinical text summarization efficiency generative artificial intelligence

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Decision Support

Background:

Triage of new hepatology referrals is time-consuming for clinicians.
Efficient review of patient records is crucial for timely diagnosis and treatment.

Purpose of the Study:

To evaluate the effectiveness of a large language model (LLM) in generating accurate summaries of patient records for hepatology referral triage.
To assess the impact of AI-generated summaries on clinician review time and accuracy.

Main Methods:

Developed a comprehensive list of data elements for referral triage.
Utilized iterative prompt engineering to instruct an LLM for data extraction from patient referral documents.
Generated AI summaries for 50 patient records and compared triage time and accuracy against original files.

Main Results:

AI summaries were significantly shorter (median 2 pages vs. 23 pages) than original patient records.
AI summaries demonstrated high accuracy (median 94.6%) with a low hallucination rate.
Clinician triage time was reduced by 60% using AI summaries (37.2 seconds vs. 94.2 seconds).

Conclusions:

LLM-generated summaries significantly reduce document length and clinician review time while maintaining accuracy.
AI summaries show promise for improving efficiency in specialist referral triage.
Future work includes integrating automated AI workflows into electronic health records for broader clinical adoption.