Evaluating online doctor–patient communication quality and its effects: Text analysis approach

Objective Building upon information quality assessment frameworks and social exchange theory, this study quantifies online doctor–patient communication quality across three dimensions using text analytics methods. Focusing on gynecological cancers online consultations, we examine how communication quality affects patient choice and satisfaction, while simultaneously investigating the moderating roles of service price and doctor engagement. Methods This study collected 19,392 doctor–patient interaction records from a leading Chinese online health platform through web crawling. Employing an interdisciplinary methodology integrating natural language processing, machine learning, and sentiment analysis for variable measurement, this study conducted empirical analyses using multivariate linear regression models. Results The study reveals that all three communication quality dimensions (thematic similarity, terminological similarity, and emotional expression similarity) exert significant positive effects on both patient choice and satisfaction. Furthermore, online service price and doctor engagement are found to positively moderate the relationship between communication quality and doctor performance, suggesting that higher-priced services and more engaged doctors amplify the beneficial impact of quality communication. Conclusions This research contributes to the theoretical understanding of doctor–patient communication in digital healthcare by extending existing frameworks and providing robust empirical evidence. The findings offer new insights into the mechanisms underlying online medical interactions and present fresh perspectives for optimizing communication models. From a practical standpoint, the study provides actionable recommendations for platform operators, including the implementation of targeted communication training programs for doctors to enhance their ability to deliver relevant and comprehensible information, as well as the development of terminology conversion systems to facilitate patient understanding of medical jargon, thereby improving overall communication quality.