Proof of concept: a practical approach to machine learning in video consultations’ acceptance in higher education institutions

Abstract

Video consultations and telemedicine play a key role in healthcare. It is crucial to assist institutions in identifying which patients are more likely to use them, increasing the adoption rate. Even though Artificial Intelligence models are important, certain cases require a human centric approach to inform patients and doctors, ensuring trust in the tools provided. This can be achieved through interpretable Machine Learning models and Statistical Analysis. In this paper we show that even though the accuracy of the Machine Learning models used (Decision Trees, Logistic Regression, and Random Forest) was between 84%-89% on the training set and 69%-76% on the test set the models lacked good ability to identify the users. Afterwards, additional statistical analysis evidenced that the main driver to use video consultations in a sample of participations with a higher-than-average education is the presence of one or more chronic diseases.