Background: No-shows can negatively affect patient care. Efforts to predict high-risk patients are needed. Previously, our epilepsy clinic identified patients with 2 or more no-shows or late cancelations in the past 18 months as being at high risk for no-shows. Our objective was to develop a model to accurately predict the risk of no-shows among patients with epilepsy seen at our neurology clinic. Methods: Using electronic health record data, we developed a least absolute shrinkage and selection operator (LASSO)–regularized logistic regression model to predict no-shows and compared its performance with our neurology clinic's above-mentioned ad hoc rule. Results: The ad hoc rule identified 13% of patients seen at our neurology clinic as high-risk patients for no-shows and resulted in a positive predictive value of 38%. In comparison, our LASSO model resulted in a positive predictive value of 48%. Our LASSO model identified that lack of private insurance, inactive Epic MyChart, greater past no-show rates, fewer appointment changes before the appointment date, and follow-up appointments were more likely to result in no-shows. Conclusions: Our LASSO model outperformed the ad hoc rule used by our neurology clinic in predicting patients at high risk for no-shows. Social workers can use the no-show risk scores generated by our LASSO model to prioritize high-risk patients for targeted intervention to reduce no-shows at our neurology clinic.

中文翻译：

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在学术儿科神经病学诊所中预测患者未出现

背景：未出现会对患者护理产生负面影响。需要努力预测高危患者。此前，我们的癫痫诊所将过去 18 个月内有 2 次或以上未出现或延迟取消的患者确定为未出现的高风险。我们的目标是开发一个模型，以准确预测在我们的神经病学诊所就诊的癫痫患者未就诊的风险。方法：使用电子健康记录数据，我们开发了一个最小绝对收缩和选择算子 (LASSO) 正则化逻辑回归模型来预测未出现，并将其性能与我们神经病学诊所的上述临时规则进行比较。结果：临时规则将在我们神经科诊所就诊的 13% 的患者确定为未就诊的高风险患者，并产生 38% 的阳性预测值。相比之下，我们的 LASSO 模型的阳性预测值为 48%。我们的 LASSO 模型发现，缺乏私人保险、不活跃的 Epic MyChart、过去的未出现率较高、约会日期前的约会更改较少以及后续约会更有可能导致未出现。结论：我们的 LASSO 模型在预测高风险未就诊患者方面优于我们神经科诊所使用的临时规则。社会工作者可以使用我们的 LASSO 模型生成的未出现风险评分来优先考虑高风险患者进行有针对性的干预，以减少我们神经科诊所的未出现。