What drives patients to choose a physician online? A study based on tree models and SHAP values

Smart healthcare is changing our lives. As an emerging medical pattern, online medical platform is arising from the combination of traditional medical resources and Internet platform, which largely resolve the disequilibrium of offline medical resources in China. Compared with offline healthcare, online platforms shorten the distance between patients and medical resources and give patients more options to seek medical treatment during the COVID-19 epidemic. In order to better help and guide patients in making decisions, the platform provides physicians’ treatment information for patients’ reference. This information describes the physician’s diagnostic capability and service level from different dimensions, such as the physician’s specialty, the number of gifts received from patients, etc., which are important basis for patients to choose a physician. For the platform and physicians, it is crucial to understand patients’ preferences for different characteristics of physicians in the consultation process, in order to manage data more targeted. This paper use machine learning methods to build a prediction model of physician’s characteristics data on incremental volume of consultation to study patients’ preferences in medical consultation. Most existing studies use linear models, but given the complexity of patient preferences, they may have greater limitations in reflecting patients’ choice logic. Therefore, this paper turn to more complex models on the training data. For the lack of interpretation of complex models, this paper uses a Shapley Value-based approach to parse the model’s feature contributions to obtain patients’ preferences for physician information. From the perspectives of local interpretation, global interpretation and interaction effect, this paper obtains regular conclusions on patients’ preferences for physicians’ information, and discusses the management insights in the context of online platform management and physicians’ word-of-mouth maintenance.