Zheng Caijiao, Zhang Yi, Lian Xiaolong, Ke Jinxiu, Chen Hongxia, Chen Yiwen
Department of Outpatient, Quanzhou First Hospital, Quanzhou, Fujian, China.
Office of the President, Quanzhou First Hospital, Quanzhou, Fujian, China.
Front Health Serv. 2025 Aug 26;5:1610004. doi: 10.3389/frhs.2025.1610004. eCollection 2025.
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.
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.
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.
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.
在医疗服务行业，患者投诉不仅是评估医院服务质量的关键指标，也是医院高质量发展的重要驱动力。通过在投诉处理过程中系统分析患者对医院管理的看法、意见和情绪反应。
因此，本文旨在开发一种医院投诉处理分析模型，以更精准地提高公众满意度。首先，使用数据清理、机械压缩、分词和停用词过滤技术对2022年1月至2024年12月期间各医院的投诉数据进行预处理。其次，采用DISC行为语言模型分析关键指标，包括医院赔偿频率、赔偿总额、患者申诉率、投诉人对解决过程的满意度以及他们对投诉结果的总体满意度。最后，将情感分析模型和改进的KANN-DBSCAN聚类模型应用于投诉数据，以精确识别与情感相关的关键词并评估负面情绪的强度，为医院提供有针对性的改进建议。
本研究将DISC行为模型应用于医疗投诉。基于DISC的文本分析实现了针对性回应。在334例干预病例和341例对照病例中，干预组满意度为93.39%，高于对照组的83.24%，表明通过基于行为的沟通策略改善了投诉解决情况。
通过分析患者的心理需求和期望，本研究旨在在提高投诉解决总体满意度的同时，尽量减少经济赔偿并降低患者申诉率，为医疗机构提供更全面、有效和个性化的投诉处理策略，同时改善患者的就医体验。
