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

通过过滤具有“完整数据”的患者的电子健康记录而引入的偏差

• 批准号: 10254420
• 负责人: Griffin M Weber
• 金额: $ 35.69万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-04 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10254420
• 关键词: Address; Adopted; Adoption; Age; Characteristics; Clinical; Clinical Trials; Clinical Trials Network; Clinical and Translational Science Awards; Code; Computer software; Country; Data; Data Set; Databases; Diagnosis; Electronic Health Record; Enrollment; Ensure; Equilibrium; Event; Funding; Geographic Locations; Goals; Gold; Health; Health care facility; Healthcare; Hospitalization; Hospitals; Individual; Institution; Insurance Carriers; Israel; Link; Machine Learning; Measures; Medical; Medical History; Medical center; Modeling; Ontology; Patients; Performance; Probability; Procedures; Provider; Race; Recording of previous events; Records; Research; Research Personnel; Sample Size; Site; Statistical Models; System; Testing; Training; United States National Institutes of Health; Visit; Weight; Work; care seeking; clinical database; cohort; cost; demographics; improved; insurance plan; open source; patient health information; patient population; personalized medicine; predictive modeling; sex

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.

项目摘要 在全国范围内采用电子健康记录（EHRS）已导致大型临床的供应量增加 数据集。借助统计建模和机器学习，这些数据集已在各种范围内使用 应用，包括诊断，决策支持，降低成本和个性化医学。然而， 因为可以在多个医疗机构接受同一患者接受治疗，所以只有一个EHR的数据可能 不包含该患者的完整病史，可能缺少重大事件。常见 解决此问题的方法是应用数据检查，以过滤EHR的数据出现的患者 更“完整”。过滤器的示例包括每年至少需要一次访问或确保年龄，性别， 和种族都记录下来。但是，在先前使用来自七个机构的EHR数据的研究中，我们表明 这些过滤器可以大大减少样本量，并通过选择患病患者来引入意外偏见 谁寻求更频繁的关心并改变由此产生的人群的人口统计。这个项目扩展了这一点 通过实施扩展的数据完整性过滤器并测试其准确性和 使用国家索赔数据和EHR数据组合的潜在偏见 全国医疗保健中心。这将使我们能够了解数据完整性如何变化 不同的EHR并量化了不同方法的权衡，以纠正患者记录中的差距。第一的， 我们将使用国家索赔数据来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”，在此期间已知患者的完整病史 他们参加了保险计划。在对提供商组进行分区以建模差距之后 在EHR数据中，我们将测试数据完整性过滤器的单独和组合机器学习 模型，选择差距较少的患者。然后，我们将通过选择病人来测试过滤器是否引入偏见 患者（更多诊断，更多的访问等）或改变其人口特征（年龄，性别和拉链 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，然后在一个国家 57个机构的网络，代表不同的地理区域，患者人口，年限 数据和医疗机构的类型。我们将通过测量它们是否改善过滤器来评估过滤器 用于预测住院的机器学习模型的性能。我们的最终目标是（a）帮助 研究人员平衡了对完整数据的需求与这可能引入其模型的偏见和（b）帮助 他们预测在一个EHR数据集上训练的模型如何在其他EHR上使用不同的数据 完整概况。