The Effects of Online Text Comments on Patients’ Choices: The Mediating Roles of Comment Sentiment and Comment Content

Abstract

As an increasingly important application of mobile social media usage, online healthcare platforms provide a new avenue for patients to obtain and exchange information, referring not only to online doctor’s advice but also to the patients’ comments on a doctor. Extant literature has studied the patients’ comments facilitated with the direct numeric information gathered in the web pages including the frequencies of “thanks letter,” “flowers,” and “recommendation scores.” Adopting the text analysis method, we analyzed patients’ comments on the healthcare platform, focusing on the comments from two aspects, namely, comment contents and content sentiment. Based on the analysis of the data collected from one of the most popular healthcare apps named “Haodaifu” in China, the results show that the vast majority of the comments are positive, which basically follows the L-shaped distribution. Meanwhile, comment sentiment covering sentiment tendency and proportion of positive comments demonstrates significant effects on recent 2-week consultation by a doctor. One of the comment contents “patience explanation” has significant effects both on the total consultation and recent 2-week consultation by a doctor. The research findings indicate that the online preferences for and evaluations on doctors provide strong support and guidance for improving doctor-patient relationships and offer implications for medical practices and healthcare platforms improvement.