@inproceedings{b91ad280706c4c07a77f92e8110f6283,
title = "Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital",
abstract = "No-show visits are a serious problem for healthcare centers. It costs a major hospital over 15 million dollars annually. The goal of this paper was to build machine learning models to identify potential no-show telemedicine visits and to identify significant factors that affect no-show visits. 257,293 telemedicine sessions and 152,164 unique patients were identified in Mount Sinai Health System between March 2020 and December 2020. 5,124 (2%) of these sessions were no-show encounters. Extreme Gradient Boosting (XGB) with under-sampling was the best machine learning model to identify no-show visits using telemedicine service. The accuracy was 0.74, with an AUC score of 0.68. Patients with previous no-show encounters, non-White or non-Asian patients, and patients living in Bronx and Manhattan were all important factors for no-show encounters. Furthermore, providers' specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.",
keywords = "No-show visits, supervised machine learning, telemedicine",
author = "Wanting Cui and Joseph Finkelstein",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
doi = "10.3233/SHTI220729",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "328--331",
editor = "John Mantas and Parisis Gallos and Emmanouil Zoulias and Arie Hasman and Househ, {Mowafa S.} and Marianna Diomidous and Joseph Liaskos and Martha Charalampidou",
booktitle = "Advances in Informatics, Management and Technology in Healthcare",
address = "Netherlands",
}