Goffman Rachel M, Harris Shannon L, May Jerrold H, Milicevic Aleksandra S, Monte Robert J, Myaskovsky Larissa, Rodriguez Keri L, Tjader Youxu C, Vargas Dominic L
Veterans Engineering Resource Center, VA Pittsburgh Healthcare System, 1010 Delafield Road 001VERC-A, Pittsburgh, PA 15215.
Fisher College of Business, The Ohio State University, 636 Fisher Hall, 2100 Neil Avenue, Columbus, OH 43210.
Mil Med. 2017 May;182(5):e1708-e1714. doi: 10.7205/MILMED-D-16-00345.
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.
Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments.
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.
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).
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.
失约降低了医疗保健系统的效率，并对所有患者获得医疗服务产生负面影响。识别有失约风险的患者有助于医疗保健系统和提供者更好地针对性采取干预措施，以减少患者爽约情况。
我们的目标是开发并测试一种预测模型，以识别有高概率错过门诊预约的患者。
人口统计学信息、预约特征和就诊历史数据取自六个不同服务区域内四个退伍军人事务医疗保健机构的现有数据集。过去的就诊行为使用基于之前最多10次预约的经验马尔可夫模型进行建模。我们使用逻辑回归开发了24种独特的预测模型。我们实施了这些模型，并通过在预约前24、48和72小时拨打实时提醒电话来测试一种干预策略。该试点研究针对1754名高风险患者，他们错过预约的概率预计至少为0.2。
我们的结果表明，在所有24个模型中，有三个变量始终与患者的爽约概率相关：过去的就诊行为、预约时间以及当天安排了多个预约。实施干预措施后，试点组的爽约率从预期的35%降至12.16%（p值<0.0001）。
该预测模型准确识别出了更有可能错过预约的患者。在实际应用该模型可使诊所对高风险患者采取更密集的干预措施。
