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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

基本信息

批准号：
10475168
负责人：
Griffin M Weber
金额：
$ 35.81万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
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/10475168
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 National Center for Advancing Translational Sciences 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 machine learning model machine learning prediction open source patient health information patient population personalized medicine predictive modeling sex

项目摘要

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.

项目总结