Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2
Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2
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DOI：
10.1093/milmed/usaa095
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发表时间：
2020-07-01
影响因子：
1.2
通讯作者：
Scott, Brianna
中科院分区：
文献类型：
--
作者：
Milicevic, Aleksandra Sasha;Mitsantisuk, Kannop;Scott, Brianna
前言不出现对病人的健康和医疗保健系统都是有害的。文献记录的未出现率范围从初级保健诊所的10%到精神卫生诊所的60%以上。我们的模型预测的概率，心理健康诊所门诊预约将不会完成，并确定可操作的变量与降低no-show.Materials和MethodsWe的概率被授予访问去识别的行政数据从退伍军人管理局企业数据仓库有关的任命在13个退伍军人管理局医疗中心。我们的建模数据集包括计划在2013年1月1日至2017年2月28日之间发生的1，206，271个唯一预约记录。训练集包括2013年1月1日至2015年12月31日期间安排的846，668个预约记录。测试集包括2016年1月1日至2017年2月28日期间安排的359，603条预约记录。因变量是预约是否完成。独立变量被分为七个集群：患者的人口统计学，预约特征，患者的出勤史，酒精使用筛查评分，药物和药物占有率，先前的诊断，以及过去利用退伍军人健康管理局服务。我们使用基于似然比的前向逐步选择来选择模型中的变量。预测模型使用SAS HPLOGISTIC proceeding.ResultsThe是否有人会错过约会的最佳指标是他们的历史出勤行为。与更高的未出现概率相关的前三个变量是：在当前预约之前的前2年的未出现率，从马尔可夫模型得出的未出现概率，以及预约的年龄。降低未就诊机会的前三个变量是：预约是新的咨询，预约是超额预约，患者在同一天有多个预约。训练数据集的受试者工作特征曲线下的平均面积为0.7577，和0.7513的测试集。结论国家倡议，以减少错过的机会-2证实的发现，以前的病人出勤率是未来出勤率的关键预测因素之一，并提供了一个额外的复杂性层分析病人的过去行为对未来的影响，出席情况.《国家减少错失机会倡议-2》规定，预约就诊与药物依从性有关，特别是用于治疗情绪障碍或阻断阿片类药物作用的药物。然而，没有办法确认病人是否真的按处方服药。因此，低药物拥有率是一个信息，虽然不是一个完美的措施。我们的目的是进一步探索如何更好地捕捉诊断和药物治疗，并用于对未出现的预测建模。我们的研究结果的影响，不同的因素对未显示率可以用来预测个人的未显示概率，并确定谁是高风险的错过预约的患者。预测患者错过预约的风险的能力将允许高级干预以减少不出现和更有效的调度。
IntroductionNo-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show.Materials and MethodsWe were granted access to de-identified administrative data from the Veterans Administration Corporate DataWarehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure.ResultsThe best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set.ConclusionsThe National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling.