Department of Industrial & Systems Engineering, Wayne State University, Detroit, MI 48202, USA.
Health Care Manag Sci. 2011 Jun;14(2):146-57. doi: 10.1007/s10729-011-9148-9. Epub 2011 Feb 1.
The number of no-shows has a significant impact on the revenue, cost and resource utilization for almost all healthcare systems. In this study we develop a hybrid probabilistic model based on logistic regression and empirical Bayesian inference to predict the probability of no-shows in real time using both general patient social and demographic information and individual clinical appointments attendance records. The model also considers the effect of appointment date and clinic type. The effectiveness of the proposed approach is validated based on a patient dataset from a VA medical center. Such an accurate prediction model can be used to enable a precise selective overbooking strategy to reduce the negative effect of no-shows and to fill appointment slots while maintaining short wait times.
失约人数对几乎所有医疗保健系统的收入、成本和资源利用都有重大影响。在这项研究中，我们开发了一种基于逻辑回归和经验贝叶斯推断的混合概率模型，使用一般患者的社会人口统计学信息和个体临床预约就诊记录，实时预测失约的概率。该模型还考虑了预约日期和诊所类型的影响。该方法的有效性是基于退伍军人事务医疗中心的患者数据集进行验证的。这种准确的预测模型可用于实现精确的选择性超额预订策略，以减少失约的负面影响，并在保持较短等待时间的同时填补预约时段。
