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Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative

Mahshad Koohi Habibi Dehkordi¹, Yehoshua Perl¹, Fadi P Deek²

¹Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, United States.

JMIR Medical Informatics

|July 24, 2025

Summary

This summary is machine-generated.

Highlighting detailed information in electronic health record (EHR) notes before summarization by large language models (LLMs) significantly improves summary accuracy and completeness. This method enhances the readability of discharge summaries for patients.

Keywords:

AI ChatGPT ChatGPT summaries EHR EHR summaries LLM LLM summaries accuracy of summaries artificial intelligence clinical notes summarization discharge notes discharge notes summarization electronic health record highlighted EHR notes large language model

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Published on: May 9, 2019

Area of Science:

Natural Language Processing
Medical Informatics
Artificial Intelligence

Background:

The American Medical Association recommends simplifying electronic health record (EHR) notes for patient readability.
Large language models (LLMs) show promise in summarizing EHR notes but can introduce inaccuracies.
This study focuses on the initial step of simplifying discharge notes using LLMs.

Purpose of the Study:

To test if LLM-generated summaries of highlighted discharge notes are more accurate than summaries of original notes.
To evaluate the impact of highlighting detailed information on summary quality.

Main Methods:

15 discharge notes from MIMIC III were sampled and detailed information was highlighted using a machine learning-developed interface terminology.
GPT-4o was used with prompt engineering to generate summaries from both highlighted and unhighlighted notes.
Summaries were manually evaluated for completeness, correctness, and structural integrity.

Main Results:

Summaries from highlighted notes (H-summaries) achieved 96% completeness, 8% higher than unhighlighted notes (U-summaries) (P=.01).
H-summaries demonstrated better correctness with fewer errors (2 vs 3) and less misplaced information (2 vs 8).
Statistical significance was observed for completeness (P=.01) and header accuracy (P=.03).

Conclusions:

Using highlighted discharge notes with LLMs and prompt engineering enhances summary quality.
This approach improves correctness, completeness, and structural integrity compared to using unhighlighted notes.
Highlighting detailed information is a key step in generating accurate and simplified EHR summaries.