Voice or text? The role of physician media choice on patient experience in online medical communities

Online medical communities (OMCs) are a type of online healthcare, in which physician-patient interaction can be comprised of a variety of media options such as pictures, text, and voice. These media formats are often used to create a personalized patient experience in AI-driven conversational healthcare platforms. To explore how physician media usage affects patient experience, we propose a counterfactual causal inference model using AI-driven data mining methods on 131,083 online consultation records and 7,666,111 messages sent by physicians from one of the largest OMCs in China. Our study reveals the negative impact of physician use of voice on patient experience, compared to text. Drawing upon social support theory, we identify the mechanism by which physician media usage for voice produces a negative effect. The findings indicate that the negative effect of physicians' voice-media usage occurs mainly in low-risk disease conditions, by weakening the role of professional and emotional support. In contrast, in high-risk disease conditions, voice-media usage strengthens the role of professional and emotional support in improving the patient's experience. Our study is one of the first to focus on the social support attributes of the different media formats used in OMCs. We use advanced AI text-analysis algorithms to extract features related to social support in physician-patient conversations, and thus contribute to the use of AI in feature extraction for research.