Patient Activeness During Online Medical Consultation in China: Multilevel Analysis.

BACKGROUND: Online medical consultation is an important complementary approach to offline health care services. It not only increases patients' accessibility to medical care, but also encourages patients to actively participate in consultation, which can result in higher shared decision making, patient satisfaction, and treatment adherence. OBJECTIVE: This study aims to explore multilevel factors that influence patient activeness in online medical consultations. METHODS: A data set comprising 40,505 patients from 300 physicians in 10 specialties was included for multilevel analysis. Patient activeness score (PAS) was calculated based on the frequency and the proportion of patient discourses to the total frequency of doctor-patient interactions. Intraclass correlation coefficients were calculated to identify between-group variations, and the final multilevel regression model included patient- and physician-level factors. RESULTS: Patients were not equally active in online medical consultations, with PASs varying from 0 to 125.73. Patient characteristics, consultation behavioral attributes, and physician professional characteristics constitute 3 dimensions that are associated with patient activeness. Specifically, young and female patients participated more actively. Patients' waiting times online (ß=-.17; P<.001) for physician responses were negatively correlated with activeness, whereas patients' initiation of conversation (ß=.83; P<.001) and patient consultation cost (ß=.52; P<.001) in online medical consultation were positively correlated. Physicians' online consultation volumes (ß=-.10; P=.01) were negatively associated with patient activeness, whereas physician online consultation fee (ß=.03; P=.01) was positively associated. The interaction effects between patient- and physician-level factors were also identified. CONCLUSIONS: Patient activeness in online medical consultation requires more scholarly attention. Patient activeness is likely to be enhanced by reducing patients' waiting times and encouraging patients' initiation of conversation in online medical consultation. The findings have practical implications for patient-centered care and the improvement of online medical consultation services.

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend.

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.