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A statistical quality assessment method for longitudinal observations in electronic health record data with an application to the VA million veteran program.

Wang H, Belitskaya-Levy I, Wu F, Lee JS, Shih MC, Tsao PS, Lu Y, VA Million Veteran Program. A statistical quality assessment method for longitudinal observations in electronic health record data with an application to the VA million veteran program. BMC medical informatics and decision making. 2021 Oct 20; 21(1):289.

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Abstract:

BACKGROUND: To describe an automated method for assessment of the plausibility of continuous variables collected in the electronic health record (EHR) data for real world evidence research use. METHODS: The most widely used approach in quality assessment (QA) for continuous variables is to detect the implausible numbers using prespecified thresholds. In augmentation to the thresholding method, we developed a score-based method that leverages the longitudinal characteristics of EHR data for detection of the observations inconsistent with the history of a patient. The method was applied to the height and weight data in the EHR from the Million Veteran Program Data from the Veteran's Healthcare Administration (VHA). A validation study was also conducted. RESULTS: The receiver operating characteristic (ROC) metrics of the developed method outperforms the widely used thresholding method. It is also demonstrated that different quality assessment methods have a non-ignorable impact on the body mass index (BMI) classification calculated from height and weight data in the VHA's database. CONCLUSIONS: The score-based method enables automated and scaled detection of the problematic data points in health care big data while allowing the investigators to select the high-quality data based on their need. Leveraging the longitudinal characteristics in EHR will significantly improve the QA performance.





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