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Online healthcare platforms serve not just as a medical knowledge-sharing community but also bring about effective interactions between professional physicians and patients. However, it is unclear whether online technology adoption affects such interactions in the same way between traditional Chinese medicine and modern medical departments. By utilizing a large sample of online doctor-patient interaction information from 168,870 doctor-specific interactive webpages recorded in a famous Chinese online healthcare community, this paper studies the differences between 17,513 traditional medicine doctor homepages and 151,357 others from more than 100 different specialty areas. Our chosen platform is representative since it covers about 800,000 physicians working at over 10,000 hospitals across all major provincial regions in China. We document that online medical service users tend to accept and use online health care services. However, patients seeing Chinese medicine doctors exhibit the following unique characteristics. They still prefer choosing doctors according to third-party information and may be reluctant to pay for the current online service price level. This problem is hard to overcome by the platform in the short run. Patients need a long-term process to adapt to the upgraded medical environment gradually. Therefore, establishing a personalized doctor recommendation system has become the most urgent demand presently. [ABSTRACT FROM AUTHOR]

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. [ABSTRACT FROM AUTHOR]