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© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background:Accurately measuring the health care needs of patients with different diseases remains a public health challenge for health care management worldwide. There is a need for new computational methods to be able to assess the health care resources required by patients with different diseases to avoid wasting resources.

Objective:This study aimed to assessing dissatisfaction with allocation of health care resources from the perspective of patients with different diseases that can help optimize resource allocation and better achieve several of the Sustainable Development Goals (SDGs), such as SDG 3 (“Good Health and Well-being”). Our goal was to show the effectiveness and practicality of large language models (LLMs) in assessing the distribution of health care resources.

Methods:We used aspect-based sentiment analysis (ABSA), which can divide textual data into several aspects for sentiment analysis. In this study, we used Chat Generative Pretrained Transformer (ChatGPT) to perform ABSA of patient reviews based on 3 aspects (patient experience, physician skills and efficiency, and infrastructure and administration)00 in which we embedded chain-of-thought (CoT) prompting and compared the performance of Chinese and English LLMs on a Chinese dataset. Additionally, we used the International Classification of Diseases 11th Revision (ICD-11) application programming interface (API) to classify the sentiment analysis results into different disease categories.

Results:We evaluated the performance of the models by comparing predicted sentiments (either positive or negative) with the labels judged by human evaluators in terms of the aforementioned 3 aspects. The results showed that ChatGPT 3.5 is superior in a combination of stability, expense, and runtime considerations compared to ChatGPT-4o and Qwen-7b. The weighted total precision of our method based on the ABSA of patient reviews was 0.907, while the average accuracy of all 3 sampling methods was 0.893. Both values suggested that the model was able to achieve our objective. Using our approach, we identified that dissatisfaction is highest for sex-related diseases and lowest for circulatory diseases and that the need for better infrastructure and administration is much higher for blood-related diseases than for other diseases in China.

Conclusions: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.

Details

Title
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
Author
Li, Jiaxuan  VIAFID ORCID Logo  ; Yang, Yunchu  VIAFID ORCID Logo  ; Mao, Chao  VIAFID ORCID Logo  ; Pang, Patrick Cheong-Iao  VIAFID ORCID Logo  ; Zhu, Quanjing  VIAFID ORCID Logo  ; Xu, Dejian  VIAFID ORCID Logo  ; Wang, Yapeng  VIAFID ORCID Logo 
First page
e66344
Section
Physician and Health Services Rating by Consumers
Publication year
2025
Publication date
2025
Publisher
Gunther Eysenbach MD MPH, Associate Professor
e-ISSN
1438-8871
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3222368446
Copyright
© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.