Biases introduced by filtering electronic health records for patients with "complete data"

项目总结 在全国范围内采用电子健康记录(EHR)导致越来越多的大型临床 数据集。通过统计建模和机器学习，这些数据集已广泛应用于 应用，包括诊断、决策支持、降低成本和个性化医疗。然而， 由于同一患者可以在多个医疗保健机构接受治疗，因此只有一个电子病历的数据可能 不包含该患者的完整病史，可能缺少关键事件。一种常见的 解决此问题的方法是应用数据检查，以筛选数据出现的患者的EHR 变得更“完整”。过滤器的例子包括要求每年至少访问一次或确保年龄、性别、 和种族都被记录下来了。然而，在之前一项使用七家机构的EHR数据的研究中，我们表明 这些过滤器可以极大地减少样本大小，并通过选择病情较重的患者来引入意想不到的偏差 他们更频繁地寻求护理，并改变了由此产生的队列的人口结构。该项目扩展了这一点 通过实施一组扩展的数据完整性筛选器并测试其准确性和 使用来自数十家医院的国家索赔数据和电子病历数据的组合，以及 全国各地的医疗保健中心。这将使我们能够了解数据完整性在 不同的电子病历和量化不同方法的权衡，以纠正患者记录中的差距。第一, 我们将使用国家索赔数据开发和衡量数据完整性过滤器的准确性。这提供了 纵向数据的“黄金标准”，其中患者的完整病史在这段时间内是已知的 他们参加了保险计划。按提供程序组对数据进行分区以建立差距模型后 在EHR数据中，我们将测试数据完整性在单独和组合机器学习中过滤的情况 模特，选择间隙较小的患者。然后，我们将通过选择病态过滤器来测试过滤器是否会引入偏差 病人(更多的诊断，更多的就诊，等等)或改变他们的人口统计特征(年龄、性别和邮编 代码)。然后，我们将在EHR数据上测试过滤器，首先在一个大型医疗中心，然后在全国范围内 由57个机构组成的网络，代表不同的地理区域、患者群体、年限 数据，以及医疗保健设施的类型。我们将通过测量这些过滤器是否改善了 机器学习模型在医院入院预测中的表现。我们的最终目标是：(A)帮助 研究人员权衡了对完整数据的需求和这可能给他们的模型带来的偏差，并(B)帮助 他们预测在一个电子病历数据集上训练的模型在具有不同数据的其他电子病历上的效果如何 完整性配置文件。

PROJECT SUMMARY Nationwide adoption of electronic health records (EHRs) has led to the increasing availability of large clinical datasets. With statistical modeling and machine learning, these datasets have been be used in a wide range of applications, including diagnosis, decision support, cost reduction, and personalized medicine. However, because the same patient could be treated at multiple health care institutions, data from only a single EHR might not contain the complete medical history for that patient, with critical events potentially missing. A common approach to addressing this problem is to apply data checks that filter the EHR for patients whose data appear to be more “complete”. Examples of filters include requiring at least one visit per year or ensuring that age, sex, and race are all recorded. However, in a previous study using EHR data from seven institutions, we showed that these filters can greatly reduce the sample size and introduce unexpected biases by selecting sicker patients who seek care more often and changing the demographics of the resulting cohorts. This project extends this prior research by implementing an expanded set of data completeness filters and testing their accuracy and potential biases using a combination of national claims data and EHR data from dozens of hospitals and healthcare centers across the country. This will enable us to understand how data completeness varies in different EHRs and quantify the tradeoffs of different approaches to correcting for gaps in patients' records. First, we will develop and measure the accuracy of data completeness filters using national claims data. This provides a “gold standard” of longitudinal data where patients' complete medical histories are known during the periods in which they were enrolled in the insurance plan. After partitioning the data by provider groups to model gaps in EHR data, we will test how well data completeness filters, individually and in combined machine learning models, select patients with fewer gaps. We will then test whether the filters introduce biases by selecting sicker patients (more diagnoses, more visits, etc.) or changing their demographic characteristics (age, sex, and zip code). Then, we will test the filters on EHR data, first at a single large medical center, and then across a national network of 57 institutions, representing different geographic regions, patient populations, number of years of data, and types of health care facilities. We will evaluate the filters by measuring whether they improve the performance of a machine learning model for predicting hospital admissions. Our ultimate goals are to (a) help researchers balance the need for complete data with the biases this might introduce to their models and (b) help them predict how well models trained on one EHR dataset might work on other EHRs with different data completeness profiles.