| With the vigorous promotion of internet medicine,the online health consultation service mode has gradually diversified.The richness of online health consultation forms and the increasing number of doctors who provide online services lead to more complicated choices for patients when conducting online health consultations.Online doctor review,given by patients after receiving online health consultation,is considered a useful indicator for patients.Patients would use it to select online health consultation services.At the same time,as a doctor’s online reputation,online doctor review is also linked to the doctor’s economic interests,which is the focus of doctors.However,the actual situation and existing research have shown that online doctor reviews can be biased.Therefore,both doctors and patients have doubts about the usefulness and representativeness of online doctor reviews,causing dissatisfaction among doctors and patients.In this context,domestic and foreign studies have conducted related research on online doctor review usefulness.However,there is a lack of research on the measurement and interactive influencing factors of online doctor review deviation.Additionally,most relevant research focuses on one-to-one online health consultation but lacks analysis of online doctor reviews under other online health consultation modes.Therefore,based on the real problems and the research gaps,this paper carried out the following research:First,from the perspective of online doctor review deviation measurement,this paper constructs an online health consultation quality evaluation model,establishes an online doctor evaluation deviation matrix,and uses actual data to analyze the deviation distribution.In particular,the quality of responses marked by a medical service platform(third-party)is used as the judgment criterion,combined with machine learning methods to build an online health consultation quality evaluation model.This paper established the online doctor review deviation matrix,comparing the online doctor review given by the patient with the actual interaction quality measurement constructed by the machine learning method.Further,this paper explored the distribution of online doctor review deviation under different modes,doctor characteristics,and patient disease characteristics.The research results in this part provide support for the follow-up research on the interactive influencing factors of online doctor review deviation.Secondly,from the perspective of online health consultation interaction design,based on the previous research,combined with the media richness theory and the framework of visual cues(MAIN model),this thesis systematically proposed and simultaneously explored three dimensions of interaction-related factors(interaction frequency,message delivery method and medical information)on online doctor review deviation.Moreover,considering the disease and psychological differences of patients in different departments,as well as the inconsistency of the usefulness of online doctor reviews through different departments in existing studies,this paper explored whether there is a difference in the influence of message delivery methods on online doctor review deviation between internal medicine and surgery.Finally,from the perspective of interactive text,based on the MAIN model,aiming at the most basic modality-text clues,combined with the anchoring effect theory,this thesis explores the influence of two types of features in doctor’s reply text(questionresponse similarity and first response similarity)on the online doctor review deviation in crowdsourcing online health consultations.On this basis,the moderating effect of response information differentiation is tested.As online health consultations have different characteristics,this paper focuses on the doctor-patient interaction content under the one-to-one online health consultation and the crowdsourcing online health consultation to conduct empirical research.This thesis uses machine learning methods to establish a quality evaluation model for online health consultation,and then construct an online doctor review deviation matrix.Through natural language processing and text analysis,this thesis extracts text features and different dimensions of interaction-related factors from interactive content.Then,through empirical analysis methods,this thesis examines the impact of interaction-related factors supported by the platform and the response text generated by doctors on online doctor review deviation.The empirical results show that:(1)One-to-one and crowdsourcing online health consultations have significant differences in the distribution of deviation types: in the one-to-one scenario,it confirms that most online doctor reviews are patient overestimation;in crowdsourcing online health consultation,most of the positive reviews can reflect the quality of doctors’ responses,but most of the doctor that provides highquality response have not received any patient feedback.In addition,for different doctor characteristics and disease characteristics,the distributions of online doctor review deviation are different.(2)Three dimensions of interaction-related factors(interaction frequency,message delivery methods,and medical information)have an impact on online doctor review deviation.There are differences in the effects of response speed and answer-question ratio on the online doctor review deviation,and the impacts of single media and integrated media are different.The findings also demonstrate that compared with internal medicine,using voice in surgery can better reduce online doctor review deviation.(3)In crowdsourcing online health consultation,the similarity between question and reply and the similarity between the following reply and first reply can increase the online doctor review deviation,and the differentiation of all reply information under the same question has the moderating effects.The results of this thesis have the following theoretical contributions and practical significance.In theory,by constructing an online doctor review deviation matrix,this thesis compares the difference between the patient evaluation results and the doctor’s online service quality and actually measures the online doctor review deviation in an online health consultation.At the same time,this thesis expands the understanding of the usefulness of online doctor review by analyzing the distribution of online doctor review deviation in different scenarios and provides support for exploring the interactive influencing factors of online doctor review deviation in online health consultations.Further,this thesis explores the influence of interface affordance on online doctor review deviation through empirical research on the interaction-related factors supported by the platform and the response text generated by doctors,and enriches the research on the influencing factors of online doctor review deviation.Through media enrichment theory and anchoring effect theory,this paper deepens the understanding of the reasons for online doctor review deviation.In practice,the online doctor review deviation matrix constructed in this thesis can be a new evaluation.At the same time,the results will help website designers to understand the interaction-related factors and the role of doctorgenerated response texts,so that can formulate coping strategies for online doctor review deviation from the perspective of data processing and evaluation mechanism improvement. |
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