Revealing Patient Dissatisfaction With Health Care Resource Allocation in Multiple Dimensions Using Large Language Models and the International Classification of Diseases 11th Revision: Aspect-Based Sentiment Analysis

The results prove that our method with LLMs can use patient reviews and the ICD-11 classification to assess the health care needs of patients with different diseases, which can assist with resource allocation rationally.

Authors

Quanjing Zhu (ORCID: https://orcid.org/0000-0003-4813-0894)
Jiaxuan Li (ORCID: https://orcid.org/0000-0001-7647-6839)
Patrick Cheong‐Iao Pang (ORCID: https://orcid.org/0000-0002-8820-5443)
Yapeng Wang (ORCID: https://orcid.org/0000-0002-1085-5091)
Chao Mao (ORCID: https://orcid.org/0009-0008-0319-4687)
Yunchu Yang (ORCID: https://orcid.org/0009-0009-1688-5695)
Dejian Xu

Institutions

Sichuan University (CN)
West China Hospital of Sichuan University (CN)
Macao Polytechnic University (MO)

Publication Details

Journal: Journal of Medical Internet Research
Published: 2025-02-04
DOI: https://doi.org/10.2196/66344
Citations: 14
Primary Topic: Patient Satisfaction in Healthcare
Type: article
Field-Weighted Citation Impact: 39.42

Revealing Patient Dissatisfaction With Health Care Resource Allocation in Multiple Dimensions Using Large Language Models and the International Classification of Diseases 11th Revision: Aspect-Based Sentiment Analysis

Authors

Institutions

Publication Details

Revealing Patient Dissatisfaction With Health Care Resource Allocation in Multiple Dimensions Using Large Language Models and the International Classification of Diseases 11th Revision: Aspect-Based Sentiment Analysis

Revealing Patient Dissatisfaction With Health Care Resource Allocation in Multiple Dimensions Using Large Language Models and the International Classification of Diseases 11th Revision: Aspect-Based Sentiment Analysis

Abstract

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