A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data | Zendy

Ziyan Yin | Zendy; Jiayi Tong | Zendy; Yong Chen | Zendy; Rebecca A. Hubbard | Zendy; Cheng Yong Tang | Zendy

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A 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) - estimator , chart , computer science , control chart , data mining , sampling (signal processing) , sample size determination , statistics , medicine , 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.

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