卡塔尔世界杯8强波胆分析

摘要

Epic Systems是美国健康信息技术的主要提供商，为2.5亿多名患者提供电子医疗记录(EMR)。Epic的平台包括病人护理的预测模型，包括一个预测病人缺席门诊保健预约的概率的模型。然而，该模型还没有在某些患者群体中进行外部验证，包括艾滋病毒感染者(PwH)。对残疾人士的定期医疗护理极为重要，残疾人士错过就诊预约与死亡率上升独立相关。我们利用芝加哥医学院2022年1月21日至3月30日期间的偶遇数据，在PwH对Epic的no-show模型进行了外部验证。我们将Epic公司预测的约会不出现的概率与这些约会的实际结果进行了比较。我们还研究了Epic模式在PwH中仅针对传染病科艾滋病毒护理预约的表现。我们进一步比较了PwH中HIV护理预约的缺席模型与我们创建的备选随机森林模型，该模型使用Epic模型中使用的7个易于获取的特征和4个与HIV临床护理或人口统计学相关的附加特征的子集。在研究期间，我们选出674名PwH，共安排了1,406次亲临预约。Epic模型在所有门诊预约的PwH中的表现AUC为0.65(0.63-0.66)。 When we restricted the data to include only HIV care clinic appointments, we identified 331 PwH who contributed 440 infectious disease appointments. The AUC of the Epic model in for HIV care appointments among PwH was 0.63 (0.59-0.67), there was no significant difference in performance compared to the model that included all appointments (P=0.36). The alternate model we created for PwH attending HIV care appointments had an AUC of 0.78 (0.75-0.82) a significant improvement over the Epic model restricted to HIV care appointments (P<0.001). Model performance among PwH was significantly lower than reported by Epic. We found that a model that incorporated a subset of the features used in the original Epic model along with demographic and HIV clinical information performed substantially better among PwH attending HIV care appointments. The inclusion of demographic factors seemed to improve the model performance substantially, indicating that among populations suffering from extreme disparities, such as PwH, inclusion of demographic information may be key to enhance the predicting prediction of difficulties in appointment attendance.