Department of Electrical and Computer Engineering, Division of Systems Engineering, Boston University, Boston, MA, USA.
Department of Radiology, Boston University School of Medicine, Boston, MA, USA.
BMC Health Serv Res. 2022 Nov 30;22(1):1454. doi: 10.1186/s12913-022-08784-8.
Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment.
This HIPAA compliant retrospective cohort study was IRB approved. Informed consent was waived. After data preprocessing steps, the dataset contained 9,970 patients and 36,606 appointments from 1/1/2015 to 12/31/2019. We identified 57 potentially impactful variables used in the initial prediction model and assessed each patient for MIA. We then developed a parsimonious model via recursive feature elimination, which identified the 25 most predictive variables. We utilized linear and non-linear models including support vector machines (SVM), logistic regression (LR), and random forest (RF) to predict MIA and compared their performance.
The highest-performing full model is the nonlinear RF, achieving the highest Area Under the ROC Curve (AUC) of 76% and average F1 score of 85%. Models limited to the most predictive variables were able to attain AUC and F1 scores comparable to models with all variables included. The variables most predictive of missed appointments included timing, prior appointment history, referral department of origin, and socioeconomic factors such as household income and access to caregiving services.
Prediction of MIA with the data available is inherently limited by the complex, multifactorial nature of MIA. However, the algorithms presented achieved acceptable performance and demonstrated that socioeconomic factors were useful predictors of MIA. In contrast with non-modifiable demographic factors, we can address SDH to decrease the incidence of MIA.
利用健康的社会决定因素（SDH）、人口统计数据和当地天气数据的预测模型被用来预测波士顿医疗中心（BMC）的乳房成像患者的错过成像预约（MIA）。患者的特征包括许多不同的变量，包括社会需求、人口统计、成像利用、预约特征和预约当天的天气条件。
这项符合 HIPAA 标准的回顾性队列研究获得了 IRB 的批准。获得了豁免知情同意。在数据预处理步骤后，数据集包含了 9970 名患者和 2015 年 1 月 1 日至 2019 年 12 月 31 日的 36606 次预约。我们确定了 57 个在初始预测模型中使用的潜在影响变量，并对每个患者进行了 MIA 评估。然后，我们通过递归特征消除开发了一个简约模型，确定了 25 个最具预测性的变量。我们利用线性和非线性模型，包括支持向量机（SVM）、逻辑回归（LR）和随机森林（RF）来预测 MIA，并比较了它们的性能。
表现最好的全模型是非线性 RF，获得了最高的 76%ROC 曲线下面积（AUC）和 85%的平均 F1 分数。限制在最具预测性变量的模型能够达到与包含所有变量的模型相当的 AUC 和 F1 分数。最能预测错过预约的变量包括时间、之前的预约历史、转诊来源科室以及家庭收入和获得护理服务等社会经济因素。
利用现有数据预测 MIA 受到 MIA 复杂、多因素性质的固有限制。然而，所提出的算法达到了可接受的性能，并表明社会经济因素是 MIA 的有用预测因素。与不可改变的人口统计因素相比，我们可以解决社会决定因素，以降低 MIA 的发生率。
