Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital

• Main Authors: Cui, W. (Author), Finkelstein, J. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2022
• Subjects: adult; article; female; human; machine learning; major clinical study; male; New York; No-show visits; nutrition; psychiatry; social worker; supervised machine learning; telemedicine
• Online Access: View Fulltext in Publisher

 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.