Research On The Impact Of Doctor-Patient Interaction On Patient Satisfaction Of Online Medical Platform

Online medical consultation is an important way for patients to obtain disease in-formation,which can help patients reduce the uncertainty of decision-making.However,few studies have focused on the relationship between online doctor-patient interaction and patient satisfaction,as well as the influence of different patient characteristics on the relationship between online doctor-patient interaction and patient satisfaction.To answer the above two questions,it is necessary to characterize the characteristics of online doctor-patient interaction.Since studies on doctor-patient interaction rarely appear in the research field of user satisfaction,it is a major challenge for this study to find the inner relationship between the two theoretically.Therefore,this study has done two parts:the satisfaction research based on doctor-patient information interaction and the satisfaction research on the moderating effect of consultation intention.Based on the satisfaction research of doctor-patient information interaction,firstly,this paper constructs the indicators to describe the online doctor-patient interaction behav-ior from the perspective of service quality theory.Then,combined with natural language processing technology and data mining method,features are extracted from doctor-patient question-and-answer data to calculate the indicators for quantifying online doctor-patient interaction behavior.Then,combining the characteristics of online doctor-patient inter-action with the characteristics of patient satisfaction,a conceptual theoretical model of doctor-patient information interaction is proposed.Finally,the theoretical hypothesis is verified by logistic regression model.The study found that because the overall response speed of doctors on the online medical platform is faster,the satisfaction of patients is not affected by the timeliness of doctor-patient information interaction.In this case,patients pay more attention to the efficiency of doctor-patient information interaction,that is,the more effective information the doctor replies or the doctor replies in a way that the patient can better understand,the higher satisfaction will be achieved.In addition,when patients are waiting for the doctor’s reply,they may have psychological behaviors to exchange the time cost for higher reply content quality.In order to study the moderating effect of consultation intention on satisfaction,firstly,this paper combined the consumer characteristics theory and the research of patients’ con-sultation intention on online medical platform to construct an indicator of consultation intention to depict the characteristics of patients.Then,combining natural language pro-cessing technology and machine learning method,this paper proposes an intention recog-nition algorithm to identify intention features from patients’ interrogation texts.Then,the relationship between the characteristics of patients’ inquisitive intention and the char-acteristics of online doctor-patient interaction and patient satisfaction was linked,and a conceptual theoretical model of the regulation of inquisitive intention was proposed.Fi-nally,the theoretical hypothesis is verified by logistic regression model.The study found that,with the increase of the importance of inquiry intention,patients pay more attention to the timeliness of doctor-patient interaction and the efficiency of information transmission in doctor-patient interaction,that is,patients prefer to spend more time on online doctors to understand their conditions,so as to provide a higher quality reply,rather than get a quick reply from doctors.There are three innovative points in this paper.First,this paper uses machine learn-ing method to describe the service process of online doctors,which solves the problem that the description of service quality is too subjective in previous studies.Secondly,this paper proposes a method based on text mining to describe the information transmission efficiency of doctor-patient interaction.This method can be used to describe the under-standing degree of information between doctors and patients.Thirdly,this paper proposes a machine learning based interrogation intention recognition algorithm,which enables re-searchers to more accurately explore the impact of different patients’ satisfaction with online medical services.