{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T08:44:00Z","timestamp":1744015440405},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,29]]},"abstract":"<jats:p>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\u2019 specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.<\/jats:p>","DOI":"10.3233\/shti220729","type":"book-chapter","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T07:33:49Z","timestamp":1656574429000},"source":"Crossref","is-referenced-by-count":3,"title":["Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital"],"prefix":"10.3233","author":[{"given":"Wanting","family":"Cui","sequence":"first","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Advances in Informatics, Management and Technology in Healthcare"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T07:33:50Z","timestamp":1656574430000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220729","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}