A telehealth application for adequate hospital visit advice by physicians affected users’ subsequent hospital visit behavior: a historical cohort study with machine learning

Abstract

IntroductionThe effectiveness of telehealth strategies toward support for adequate hospital visits is vital. We examined whether individuals who received advice from a physician via an online application subsequently visited hospitals. Further, we examined the background factors associated with their hospital visit behavior.MethodsWe used machine learning to examine whether chief complaint, medical advice, and user background characteristics could be used to predict their subsequent hospital visit.ResultsAmong 7,152 participants, those in their 30s were the most frequent users. The proportion of each medical advice was significantly different between the group that did and the one that did not follow physicians’ advice. We further performed supervised machine learning using random forest modeling to categorize those who (1) followed physicians’ advice or (2) did not follow physicians’ advice. The area under the receiver operating characteristic curve was 0.677. Consequently, the aforementioned model soundly categorized whether users followed physicians’ advice. Chief complaint and medical advice were the most important variables to predict whether users followed the advice.DiscussionThe telehealth system to provide support for adequate hospital visits influenced patients’ subsequent hospital visit behavior. Patients’ chief complaint was the most important variable in discriminating whether users followed physicians’ advice.