Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program

检查表 计算机科学 一致性 数据质量 暂时性 操作化 完备性(序理论) 质量(理念) 文档 过程管理 数据科学 数据挖掘 医学 心理学 运营管理 工程类 数学 认识论 哲学 程序设计语言 认知心理学 内科学 公制(单位) 数学分析
作者
Lewis E. Berman,Yechiam Ostchega,John Giannini,Lakshmi Priya Anandan,Emily Clark,Henry M. Spotnitz,Lina Sulieman,Michael Volynski,Andrea H. Ramirez
出处
期刊:JCO clinical cancer informatics [American Society of Clinical Oncology]
卷期号:8 (8): e2400052-e2400052 被引量:1
标识
DOI:10.1200/cci.24.00052
摘要

PURPOSE The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework. METHODS We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older. RESULTS Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality. CONCLUSION This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

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