Patient no-show predictive model development using multiple data sources for an effective overbooking approach.
Patient no-show predictive model development using multiple data sources for an effective overbooking approach.
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DOI：
10.4338/aci-2014-04-ra-0026
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发表时间：
2014-01-01
影响因子：
2.9
通讯作者：
Hanauer, D A
中科院分区：
文献类型：
--
作者：
Huang, Y;Hanauer, D A
背景：患者未出现在门诊递送系统中仍然是个问题。负面影响包括医疗资源利用不足，增加医疗费用，减少获得护理，降低诊所效率和供应商productivity.Objective：为了开发一个基于证据的预测模型，为病人不显示，从而改善超预约的方法在门诊设置，以减少不显示的负面影响.从一个普通儿科诊所的调度系统和电子健康记录系统中提取了10年的回顾性数据，包括7,988名不同的患者和104名，799次访问沿着有关预约特征、患者人口统计学和保险信息的变量。使用描述性统计量探索变量对显示或未显示状态的影响。使用逻辑回归来开发未出现预测模型，然后使用该模型来构建算法以确定计算预测的出现/未出现状态的未出现阈值。这种方法旨在超额预订预约，其中预定的患者被预测为不出现。该方法与两种常用的超售方法进行了比较，以证明在病人等待时间，医生空闲时间，加班和总costs.RESULTS的有效性：从训练数据集，最佳错误率为10.6%，没有出现阈值为0.74。该阈值成功预测了验证数据集，错误率为13.9%。建议的超额预订的方法表现出显着减少至少6%的病人等待，27%的加班费，和3%的总成本相比，其他常见的平面overbooking methods.CONCLUSIONS：本文演示了另一种方式来适应超额预订，占预测个别病人的显示/不显示状态。预测性未出现模型导致动态超额预订策略，该策略可以改善患者等待、加班和诊所日的总成本，同时保持完整的调度能力。
BACKGROUND: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.OBJECTIVE: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.METHODS: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.RESULTS: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.CONCLUSIONS: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.