What Affects Patients’ Online Decisions: An Empirical Study of Online Appointment Service Based on Text Mining
The emergence of online health communities enables patients’ comments on doctors to express their opinion on service and also make it possible for patients seeking doctors’ information before seeing doctor. Making appointment online and then go to see a doctor offline on schedule become popular in China due to its convenience. Both econometric estimations and text mining are used to explore the factors that influence patients’ selection of doctors in OAS. The results show that online satisfaction does affect patients to choose doctor, although offline attributes, such as doctor’s title and the tier level of hospital, are also considered. We find that overall satisfaction and review volume both have positive impacts on patients’ online decisions. As for the specific dimensions of satisfactions extracted from reviews, the service attitude, technical level, explanation clarity, and doctor ethics also positively affect the number of OAS. The moderating effect between doctor’s online recommendation and title is negative, as patients care more about doctor’s online reviews when she has a low title and vice versa. In addition, the results reveal that patients with high-risk disease are more sensitive to doctor’s review volume. Our findings can help doctors design their strategy of online appointment service, and also help online health communities refine their review system so that patients can express their attitudes more specifically.
KeywordsOnline health community Patient satisfaction Online appointment service Text mining Sentiment analysis
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