Evidence map›Paper›PMID 40933720›Full record

ArticleFrontiers in health services2025

Impact of hospital complaint handling on promoting high-quality development of hospitals via an emotional language analysis model: a case study of a tertiary hospital service center in Quanzhou city, Fujian province.

Caijiao Zheng, Yi Zhang, Xiaolong Lian, Jinxiu Ke, Hongxia Chen, Yiwen Chen

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Article in Frontiers in health services, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Caijiao ZhengDepartment of Outpatient, Quanzhou First Hospital, Quanzhou, Fujian, China.
Yi ZhangOffice of the President, Quanzhou First Hospital, Quanzhou, Fujian, China.
Xiaolong LianDepartment of Medical Affairs, Quanzhou First Hospital, Quanzhou, Fujian, China.
Jinxiu KeDepartment of Outpatient, Quanzhou First Hospital, Quanzhou, Fujian, China.
Hongxia ChenDepartment of Outpatient, Quanzhou First Hospital, Quanzhou, Fujian, China.
Yiwen ChenDepartment of Outpatient, Quanzhou First Hospital, Quanzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the healthcare service industry, patient complaints serve not only as a critical metric for assessing hospital service quality but also as a fundamental driver of high-quality hospital development. Through a systematic analysis of patients' perceptions, opinions, and emotional responses to hospital management within the complaint-handling process. Methods: Therefore, this paper aims to develop a hospital complaint-handling analysis model to enhance public satisfaction with greater precision. First, complaint data from hospitals spanning January to December 2022-2024 was preprocessed using data cleaning, mechanical compression, word segmentation, and stop-word filtering techniques. Second, the DISC behavioral language model was employed to analyze key indicators, including hospital compensation frequency, total compensation amounts, patient appeal rates, complainants' satisfaction with the resolution process, and their overall satisfaction with complaint outcomes. Finally, a sentiment analysis model and an improved KANN-DBSCAN clustering model were applied to complaint data to precisely identify sentiment-related keywords and assess the intensity of negative emotions, providing hospitals with targeted improvement recommendations. Results: This study applied the DISC behavioral model to medical complaints. DISC-based text analysis enabled tailored responses. Among 334 intervention and 341 control cases, satisfaction 93.39%, was higher in the intervention group 83.24%, indicating improved complaint resolution through behavior-informed communication strategies. Conclusions: By analyzing patients' psychological needs and expectations, this study aims to minimize financial compensation and reduce patient appeals while enhancing overall complaint resolution satisfaction, which provides medical institutions with a more comprehensive, effective, and personalized complaint-handling strategy while simultaneously improving patients' healthcare experiences.

Indexed as

data cleaningDISC behavioral language modelhospital service qualityKANN-DBSCAN clustering modelpatient complaint analysis

Identifiers

PMID40933720
PMCPMC12417483

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.