Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Stud Health Technol Inform. 2022 Jun 29;295:328-331. doi: 10.3233/SHTI220729.
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.
爽约就诊是医疗中心的一个严重问题。一家主要医院每年因此损失超过 1500 万美元。本文的目的是构建机器学习模型，以识别潜在的远程医疗爽约就诊，并确定影响爽约就诊的重要因素。2020 年 3 月至 2020 年 12 月，西奈山卫生系统共识别出 257293 次远程医疗就诊和 152164 名独特患者。其中 5124 次（2%）就诊为爽约就诊。使用远程医疗服务识别爽约就诊的最佳机器学习模型是极端梯度提升（XGB）与欠采样。准确率为 0.74，AUC 得分为 0.68。有过爽约就诊经历、非白种人或非亚洲人、以及居住在布朗克斯区和曼哈顿的患者，都是出现爽约就诊的重要因素。此外，精神科和营养科医生以及社会工作者的服务对象，出现更高的患者爽约率的可能性更大。
