基于主题词聚类和社会网络分析的患者就医体验主题挖掘

Topic mining of patient hospital-visiting experiences based on subject headings clustering and social network analysis

目的:采用主题词聚类和社会网络分析方法,从患者负面评价文本中挖掘主题分类信息,为改善医疗服务体验提供参考。方法:对华中某三甲医院2013-2019年线下回访调查获取的非结构化负面评价文本集进行Kmeans词汇聚类分析和社会网络分析,提炼出与患者诊疗服务体验相关的主题类,分析影响患者就医满意度的因素。结果:通过对来自门诊、入院和出院3种类型患者的回访文本进行词聚类后,均提取出了8个主题类。社会网络可视化图谱显示3种类型患者对服务体验的感知侧重点不同。结论:整体上,患者负面反馈在医疗服务价格透明度和服务时间规划等方面最为突出,说明医疗机构应优先提高此类更受患者关切的服务质量,营造和谐的就医环境。

Objective To mine the topic classification information from the negative assessment texts of patients based on subject headings clustering and social network analysis in order to provide reference for hospitals to improve their patient hospital-visiting experiences. Methods The unstructured negative assessment text set obtained in off-line follow-up of a central China tertiary class A hospital from 2013 to 2019 was analyzed by Kmeans words cluster analysis and social network analysis respectively. The topics related with patient hospital-visiting experiences were extracted and the factors influencing patient hospital-visiting experiences were investigated.Results Eight topics were extracted after the follow-up texts of outpatient department patients,hospitalized patients and discharged patients were clustered. Visualized map of social network showed different perceptive hospital-visiting experiences of outpatient department patients,hospitalized patients and discharged patients. Conclusion The negative feedback contents of patients are significant in transparency of medical service price and planning of service time,indicating that hospitals should give the priority to their medical service and create the hormonal hospital-visiting environment.