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. 2024 Mar 6:10:20552076241233864.
doi: 10.1177/20552076241233864. eCollection 2024 Jan-Dec.

Medical service quality evaluation based on LDA and sentiment analysis: Examples of seven chronic diseases

Jing Dai  1 , Fang Lyu  1 , Lin Yu  1 , Zixuan Zhou  1 , Yunyu He  2
Affiliations

Medical service quality evaluation based on LDA and sentiment analysis: Examples of seven chronic diseases

Jing Dai et al. Digit Health. .

Abstract

Objective: In this article, we investigate how chronic noncommunicable disease (CND) patients evaluate the medical service, and what obstacles exist in this process, which is useful for hospitals to improve efficiency and enhance patient satisfaction.

Methods: Based on the total number of CND patients in China, 7 CNDs were selected as the evaluation objects, and then selected the Haodaifu website as the data source, crawled 15,682 medical service reviews, then the 9 themes were analyzed by the LDA theme model. The evaluation index system of six indicators was constructed based on quality management theory. The binary long short-term memory model was used to analyze the sentiment, and the entropy-valued, TOPSIS and gray correlation model was implemented for medical service quality evaluation; the barrier model was used to find out the key factors limiting medical services.

Results: (a) Hypertension was rated at a good level in the degree of gray correlation closeness, bronchitis was rated at a low level and the rest were at an intermediate level. (b) The first two overall barriers were the hospitalization process and registration services which occupy about 30%, respectively. This implies that hospitals should focus on providing registration services and inpatient settings in the future.

Conclusion: To promote hospitals to provide better services for patients with CNDs and improve patient satisfaction with medical care. And it is necessary to optimize medical services fundamentally by optimizing the inpatient process and improving the registration process to improve efficiency.

Keywords: Chronic noncommunicable disease (CND); TOPSIS; barrier factor; gray correlation; medical service quality evaluation.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
A schematic framework of text data analysis method.
Figure 2.
Figure 2.
LDA model generation process.
Figure 3.
Figure 3.
BiLSTM network structure.
Figure 4.
Figure 4.
Internal structure of LSTM.
Figure 5.
Figure 5.
The result of coherence value.
Figure 6.
Figure 6.
The result of the LDA model.
See this image and copyright information in PMC

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