Effects of emotional expression and platform prompts on patient selection: A multi-method study on online question and answer platforms

Abstract

Patients lacking medical expertise in online health Q&A platforms find it difficult to assess the quality of physician responses. Emotional support provided by doctors and prompts from the platform may influence patient selection.

We developed a conceptual model based on emotions as social information theory and the Stimulus-Organism-Response framework. The model was validated using a multi-method and multi-study approach. In Study 1, we analyzed platform data using natural language processing and machine learning techniques to explore the impacts of physician expression characteristics on patient selection. In Study 2, we conducted an experiment to explore patient perceptions and influence mechanisms in the process.

Study 1 showed the effects of empathy, encouragement, and using default responses on patient selection. Study 2 revealed the importance of psychological empowerment and uniqueness neglect on patient decisions. The findings have significant implications for physicians and platforms in designing improved health services and optimizing systems.