Mieloszyk Rebecca J, Rosenbaum Joshua I, Bhargava Puneet, Hall Christopher S
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2618-2621. doi: 10.1109/EMBC.2017.8037394.
No-show appointments are a troublesome, but frequent, occurrence in radiology hospital departments and private practice. Prior work in medical appointment no-show prediction has focused on general practice and has not considered features specific to the radiology environment. We collect data from 16 years of outpatient examinations in a multi-site hospital radiology department. Data from the radiology information system (RIS) are fused with patient income estimated from U.S. Census data. Features were categorized into three groups: Patient, Exam, and Scheduling. Models based on the total feature set and separately on each feature group were developed using logistic regression to assess the per-appointment likelihood of no-show. After five-fold cross-validation, no-show prediction using the total feature set from 554,611 appointments yielded an area under the curve (AUC) of 0.770 ± 0.003. Feature groups that were most informative in the prediction of no-show appointments were those based on the type of exam and on scheduling attributes such as the lead time of scheduling the appointment. A data-driven no-show prediction model like the one presented here could be useful to schedulers in the implementation of an automated scheduling policy or the assignment of examinations with a high risk of no-show to lower impact appointment slots.
爽约在放射科医院科室和私人诊所中是一件麻烦但却频繁发生的事情。先前在医疗预约爽约预测方面的工作主要集中在普通医疗领域，并未考虑放射科环境特有的特征。我们收集了一家多院区医院放射科16年门诊检查的数据。放射信息系统（RIS）的数据与根据美国人口普查数据估算的患者收入数据相融合。特征被分为三组：患者、检查和预约安排。使用逻辑回归分别基于全部特征集以及每个特征组建立模型，以评估每次预约的爽约可能性。经过五折交叉验证，利用来自554,611次预约的全部特征集进行爽约预测，得到的曲线下面积（AUC）为0.770±0.003。在预测爽约预约方面信息最丰富的特征组是基于检查类型以及预约安排属性（如预约安排的提前时间）的那些特征组。像本文所展示的这种数据驱动的爽约预测模型，对于调度人员实施自动调度策略或将具有高爽约风险的检查分配到影响较小的预约时段可能会很有用。
