互联网医疗下患者持续线上咨询和线下就诊意愿影响因素研究

背景　随着“互联网+”的不断发展，互联网医疗消费规模急剧膨胀。而在互联网医疗的兴起下，患者健康的改善往往需要结合线下实体医院的进一步诊断和治疗。但是既往互联网医疗研究中，大部分只停留在对患者线上咨询的意愿上，很少拓展到线下就诊意愿。目的　整合公平理论和刺激-机体-反应模型（S-O-R模型），从多角度探究互联网医疗下患者持续线上咨询意愿和线下就诊意愿的影响因素，以期更加全面地了解医患之间的交流对患者线上及线下行为的影响，以及患者线上与线下行为之间的关联。方法　基于公平理论，将患者的感知公平分为分配公平、程序公平、交互公平和信息公平4个维度；基于S-O-R模型，将感知公平作为刺激（S），将患者对于医生的信任作为机体（O），而持续线上咨询意愿和线下就诊意愿则作为患者的后续反应行为（R）。并构建如下结构方程模型：H1--分配公平与感知公平呈正相关；H2--程序公平与感知公平呈正相关；H3--交互公平与感知公平呈正相关；H4--信息公平与感知公平呈正相关；H5--感知公平与信任呈正相关；H6--信任与持续线上咨询意愿呈正相关；H7--信任与线下就诊意愿呈正相关；H8--持续线上咨询意愿与线下就诊意愿呈正相关。2018年4-5月，在好大夫网站10位医生的协助下，选择曾经通过该网站对其进行过线上咨询的800例患者为调查对象。采用一般资料调查表和基于既往研究设计的问卷对其进行调查。进行量测模型的检验和结构方程模型的验证。结果　共发放问卷800份，回收有效问卷284份，有效回收率为35.5%。聚合效度：各题项的因子载荷为0.81~0.92；各构面的Cronbach's α系数为0.84~0.92；各构面的组合信度（CR）为0.90~0.94；各构面的平均提取方差值（AVE）为0.76~0.83。区分效度：每个构面AVE的平方根均大于其与其他构面AVE的平方根。分配公平（β=0.265，P<0.001）、程序公平（β=0.198，P<0.001）、交互公平（β=0.275，P<0.001）、信息公平（β=0.378，P<0.001）均与感知公平呈正相关，假设H1、H2、H3和H4全部成立；感知公平（β=0.846，P<0.001）与信任呈正相关，且信任的判定系数为71.4%，假设H5成立；信任（β=0.593，P<0.001）与持续线上咨询意愿呈正相关，且持续线上咨询意愿的判定系数为34.9%，假设H6成立；信任（β=0.368，P<0.001）与线下就诊意愿呈正相关，假设H7成立；持续线上咨询意愿（β=0.411，P<0.001）与线下就诊意愿呈正相关，且线下就诊意愿的判定系数为47.9%，假设H8成立。结论　通过整合公平理论和S-O-R模型，本研究验证了医患之间的感知公平会影响患者对医生的信任，进而对患者线上持续咨询意愿与线下就诊意愿起积极影响，且线上持续咨询意愿也会加强患者线下就诊意愿。

An Investigation of Factors Affecting Patients' Intention of Online Continuous Consultation and Offline Visiting in eHealth

Background　With the flourishing development of Internet technologies，the scale of eHealth consumption expands sharply. Patients' health not only depends on participating in emerging eHealth but also further offline diagnosis and treatment in hospitals. However，the bulk of previous studies on eHealth have merely focused on patients' online counseling intention but have ignored the role of behaviors in offline hospitals. Objective　Integrating justice theory and S-O-R model，this study aimed to investigate the factors affecting patients' continuous intention to consult online and intention to visit offline，in order to provide a comprehensive understanding of the role of doctor-patient interaction in patients' online and offline behaviors as well as the relationships between patients' online and offline behaviors. Methods　Based on justice theory，patients' perceived trust was divided into four dimensions：distributive justice，procedural justice，interpersonal justice and informational justice. Based on S-O-R model，patients' perceived justice was characterized as stimulus（S），trust was characterized as organism（O），and continuous intention to consult and the intention to visit offline hospitals were characterized as responses（R）. Then a structural equation model was constructed and developed in accordance with the following hypotheses：H1——distributive justice is positively related to perceived justice；H2——procedural justice is positively related to perceived justice；H3——interpersonal justice was positively related to perceived justice；H4——informational justice is positively related to perceived justice；H5——perceived justice is positively related to trust；H6——trust is positively related to continuous intention to consult online；H7——trust is positively related to the intention to visit offline；H8——continuous intention to consult online is positively related to the intention to visit offline. From April to May 2018，with the help of 10 doctors，800 patients who ever received online consultation services delivered by them in Haodf.com were selected and were surveyed using questionnaires developed by our research team for collecting their demographic information and factors associated with online and offline consultations. Subsequently，the measurement model and structural model were examined. Results　A total of 284 cases（35.5%）returned responsive questionnaires，with a response rate of 35.5%. And analysis of their survey results showed the measurement model's convergent validity：the factor loadings of items were from 0.81 to 0.92；Cronbach's α was 0.84 to 0.92；composite reliability was 0.90 to 0.94；average variance extracted（AVE）was 0.76 to 0.83. Discriminant validity：the square roots of AVE for each construct exceeded the correlations of other constructs. Distributive justice（β=0.265，P<0.001），procedural justice（β=0.198，P<0.001），interpersonal justice（β=0.275，P<0.001），and informational justice（β=0.378，P<0.001）positively affected perceived justice，supporting the 1st，2nd，3rd，and 4th hypotheses. Perceived justice（β=0.846，P<0.001）positively affected trust，which also explained 71.4% of the variance，supporting the 5th hypothesis. Trust（β=0.593，P<0.001）positively affected continuous intention to consult online，which also explained 34.9% of the variance，supporting the 6th hypothesis. Trust（β=0.368，P<0.001）positively affected the intention to visit offline，supporting the 7th hypothesis. Continuous intention to consult online（β=0.411，P<0.001）positively affected the intention to visit offline，which also explained 47.9% of the variance，supporting the 8th hypothesis. Conclusion　By integrating justice theory and S-O-R model，this paper validated patients' perceptions of justice significantly affecting the trust towards doctors，which in turn have the positive influence on patients' continuous intention to consult online and intention to visit offline hospitals.In addition，patients' continuous intention to consult online has a direct impact on the intention to visit offline hospitals.