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

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.