Effects of Online Medical Teams on Patients’ Choices for Doctor Selection: A Hybrid Deep Learning Framework

Abstract

Amidst the overwhelming online medical service information, patients without professional medical knowledge often struggle to identify suitable doctors in online healthcare communities.The advent of online medical teams (OMTs) as a new source of publicly available information, offers patients novel avenues to understand the features of doctors.Therefore, in this study, we aim to explore the effect of OMTs information on patients' choices for doctor selection, thereby better assisting patients in selecting a suitable doctor.We first integrate the OMTs information with the online reviews, disease descriptions, and doctor profiles to build the multi-source and multi-type medical data inputs.Based on these inputs, we develop a hybrid deep learning framework to uncover the effects of various factors on predicting patients' choices for doctor selection.The results indicate that OMTs information can significantly enhance the probability of predicting patients' choices for doctor selection.Surprisingly, the effect of OMTs information on patients' preference features is greater than that of patients' disease features in the process of doctor selection.

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