Open AccessA cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data
Author(s) -
Ziyan Yin,
Jiayi Tong,
Yong Chen,
Rebecca A. Hubbard,
Cheng Yong Tang
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab222
Subject(s) - chart , estimator , control chart , computer science , sampling (signal processing) , sample size determination , data mining , medicine , statistics , process (computing) , mathematics , filter (signal processing) , computer vision , operating system
Electronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach for designing manual chart review, where EHR-derived phenotypes can be used to guide the selection of charts to be reviewed in order to maximize statistical efficiency in the subsequent estimation of risk factor associations.