基于机器学习的门诊患者满意度影响因素分析  

Analysis of Influencing Factors of Outpatients Service Satisfaction Based on Machine Learning

作　　者：胡鹏[1] 杨媛 黄艳[1] HU Peng;YANG Yuan;HUANG Yan(Guangzhou First People’s Hospital,Guangzhou 510180,Guangdong,China)

机构地区：[1]广州市第一人民医院,广东省广州市510180

出　　处：《中国卫生信息管理杂志》2023年第6期1010-1018,共9页Chinese Journal of Health Informatics and Management

摘　　要：目的探讨影响门诊患者就医总体感受的关键因素,为针对性地改善门诊服务质量提供参考。方法运用描述性统计、单因素分析、机器学习和SHAP解释框架,分析门诊患者就医总体感受与影响因素之间的关系,并评价相关因素的重要性。结果医疗相关因素中影响门诊患者就医总体感受排名前3位的因素分别是预约挂号、问诊医生和导诊服务,患者自身相关因素中就诊科室、就诊次数、职业类别、付费方式、教育水平和居住地址对就医总体感受有影响,但重要程度不如医疗相关因素。结论机器学习模型和SHAP解释框架可用于揭示影响门诊患者就医总体感受的重点因素,帮助医院明确优先改进环节和资源投入方向,高效精准地提升门诊服务质量。Objective To discuss the key factors that affect the overall medical experience of outpatients.To provide reference for improving the quality of outpatient service.Methods Descriptive statistics,single factor analysis,machine learning model and shapley additive explanations(SHAP)interpretation framework were used to analyze the relationship between the overall feelings of outpatients and influencing factors and evaluate the importance of related factors.Results Among the relevant medical factors,the top three affecting the overall feelings of outpatients are appointment registration,consulting doctor and hospitalization guidance service,respectively.In terms of the relevant factors of patients themselves,the consultation department,the number of visits,occupation category,payment method,education level and residential address have an impact on the overall feelings of medical treatment,but they are not as significant as the relevant medical factors.Conclusion The machine learning model and SHAP interpretation framework can reveal the key factors that affect the overall feeling of outpatients and help the hospital to clarify the priority improvement links and the direction of resource investment,so as to improve the quality of outpatient service in an efficient and accurate manner.

关 键 词：机器学习 SHAP 门诊患者 满意度 影响因素 

分 类 号：R-058[医药卫生] R319