Research on the Impact of Patients'Feedback on Doctors'Service Behaviors Based on Text Mining
[Purpose/significance]With the development of internet medical platforms,the doctor-patient relationship has gradu-ally shifted towards"patient-centered".Exploring how patients'feedback affects doctors'service behaviors will help both doctors and patients better participate in online medical services.[Method/process]According to the online review data of the Good Doctor Online platform,this study uses aspect-level sentiment analysis(ABSA)based on sentiment dictionary to extract multi-dimensional review text features,builds a research model of review information volume,review text features and doctors'service behaviors,and carries out re-gression analysis.[Result/conclusion]The number of reviews can promote doctors'service behaviors more than the diversity of re-views;professional evaluation intensity can promote prosocial service behavior more than soft power evaluation intensity,while soft power evaluation intensity can promote paid consultation service behavior more than professional evaluation intensity.The research conclusion has reference significance for the improvement of doctor-patient relationship and the development of medical platform.
medical platformpatients'feedbackmulti-dimensional review text featuresprosocial service behaviorpaid consul-tation service behavior
NETL
NSTL
万方数据
