Statistical Methods and Designs for Addressing Correlated Errors in Outcomes and Covariates in Studies Using Electronic Health Records Data [Methods Study], Tennessee, 2016-2021

Electronic health records, or EHRs, have data on patient traits, health problems, and treatments. Researchers can use EHR data to study how treatments work or which patient traits affect health outcomes. But EHR data can have errors.
The best way to get accurate EHR data is to closely review patients' original records. But reviewing all patient records isn't possible when many patients are in a study. In such cases, researchers can review and correct records for a few patients and use the revised records to adjust data for all patients. But existing methods for using revised records don't address some kinds of errors, such as errors that are related. For example, errors in a treatment starting date can lead to mistakes in the data on length of treatment.
In this project, the research team created and tested new methods to improve the accuracy of EHR data. The new methods corrected records from some patients. Then the team used the corrections to address related errors for all patients.
To access the methods and software, please visit the MeasurementErrorMethods GitHub repository.