Query-Guided Self-Supervised Summarization of Nursing Notes

TL;DR

This paper introduces QGSumm, a novel query-guided self-supervised approach for abstractive nursing note summarization that does not require reference summaries, and demonstrates its effectiveness compared to state-of-the-art models.

Contribution

The paper presents a new self-supervised, query-guided method for clinical note summarization that bypasses the need for reference summaries, tailored for healthcare applications.

Findings

QGSumm achieves high-quality summaries with balanced content recall and low hallucination.

GPT-4 performs competitively in preserving original nursing note information.

QGSumm outperforms other top methods in clinical note summarization.

Abstract

Nursing notes, an important part of Electronic Health Records (EHRs), track a patient's health during a care episode. Summarizing key information in nursing notes can help clinicians quickly understand patients' conditions. However, existing summarization methods in the clinical setting, especially abstractive methods, have overlooked nursing notes and require reference summaries for training. We introduce QGSumm, a novel query-guided self-supervised domain adaptation approach for abstractive nursing note summarization. The method uses patient-related clinical queries for guidance, and hence does not need reference summaries for training. Through automatic experiments and manual evaluation by an expert clinician, we study our approach and other state-of-the-art Large Language Models (LLMs) for nursing note summarization. Our experiments show: 1) GPT-4 is competitive in maintaining information in the original nursing notes, 2) QGSumm can generate high-quality summaries with a good balance between recall of the original content and hallucination rate lower than other top methods. Ultimately, our work offers a new perspective on conditional text summarization, tailored to clinical applications.