Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration
Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration
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DOI：
10.7205/milmed-d-16-00345
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发表时间：
2017-05-01
影响因子：
1.2
通讯作者：
Vargas, Dominic L.
中科院分区：
文献类型：
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作者：
Goffman, Rachel M.;Harris, Shannon L.;Vargas, Dominic L.
背景：错过预约降低了卫生保健系统的效率，并对所有患者获得护理产生负面影响。识别有错过预约风险的患者可以帮助医疗保健系统和提供者更好地进行有针对性的干预，以减少患者的缺席。目的：我们的目的是开发和测试一个预测模型，以确定患者有高概率错过他们的门诊预约。方法：从6个独立服务区的4个退伍军人事务卫生保健机构的现有数据集中提取人口统计信息、预约特征和就诊历史。过去的出勤行为是用一个基于多达10次以前预约的经验马尔可夫模型来建模的。利用逻辑回归，我们建立了24个独特的预测模型。我们实施了这些模型，并通过提前24小时、48小时和72小时拨打实时提醒电话来测试干预策略。这项试点研究的目标是1754名高危患者，他们错过预约的概率预计至少为0.2。结果：我们的结果表明，在所有24个模型中，有三个变量与患者的缺席概率一致相关：过去的出勤行为，预约的年龄，以及当天安排了多次预约。干预实施后，试验组的缺席率从期望值35%降至12.16% （p值< 0.0001）。结论：该预测模型准确地识别了更有可能错过预约的患者。在实践中应用该模型，使诊所能够对高危患者采取更强化的干预措施。
Background: Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. Objectives: Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. Methods: Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. Results: Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). Conclusions: The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.