A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.
Objectives: 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 n...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 29; no. 1; pp. 52 - 62 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154359990&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154359990 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jan2022 vid: 29 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 154359990 154359990 NLM34718618 154359990 10.1093/jamia/ocab222 NLM34718618 154359990 ppf: 52 ppct: 10 formats: tig: atl: A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data. aug: au: Yin, Ziyan Tong, Jiayi Chen, Yong Hubbard, Rebecca A Tang, Cheng Yong affil: Department of Statistical Science, Temple University , Philadelphia, Pennsylvania, USA sug: subj: Neoplasm Recurrence, Local Human Prospective Studies Cost Benefit Analysis Phenotype Comparative Studies Multicenter Studies Funding Source ab: Objectives: 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.Materials and Methods: After applying outcome-dependent sampling, an augmented estimator can be constructed by optimally combining the chart-reviewed phenotypes from the selected patients with the error-prone EHR-derived phenotype. We conducted simulation studies to evaluate the proposed method and applied our method to data on colon cancer recurrence in a cohort of patients treated for a primary colon cancer in the Kaiser Permanente Washington (KPW) healthcare system.Results: Simulations verify the coverage probability of the proposed method and show that, when disease prevalence is less than 30%, the proposed method has smaller variance than an existing method where the validation set for chart review is uniformly sampled. In addition, from design perspective, the proposed method is able to achieve the same statistical power with 50% fewer charts to be validated than the uniform sampling method, thus, leading to a substantial efficiency gain in chart review. These findings were also confirmed by the application of the competing methods to the KPW colon cancer data.Discussion: Our simulation studies and analysis of data from KPW demonstrate that, compared to an existing uniform sampling method, the proposed outcome-dependent method can lead to a more efficient chart review sampling design and unbiased association estimates with higher statistical efficiency.Conclusion: The proposed method not only optimally combines phenotypes from chart review with EHR-derived phenotypes but also suggests an efficient design for conducting chart review, with the goal of improving the efficiency of estimated risk factor associations using EHR data. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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