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

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Abstract

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.

Abstract

A Data Quality Framework Applied to ductal carcinoma in situ EHR data.

Related collections

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Is Open Access

Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Nicole Weiskopf, Chunhua Weng (2013)

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

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Beyond Accuracy: What Data Quality Means to Data Consumers

Richard Wang, Diane Strong (2015)

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Author and article information

Contributors

Lew Berman: (View ORCID Profile)

Yechiam Ostchega: (View ORCID Profile)

John Giannini: (View ORCID Profile)

Lakshmi Priya Anandan: (View ORCID Profile)

Emily Clark: (View ORCID Profile)

Matthew Spotnitz: (View ORCID Profile)

Andrea Ramirez: (View ORCID Profile)

Journal

Title: JCO Clinical Cancer Informatics

Abbreviated Title: JCO Clin Cancer Inform

Publisher: American Society of Clinical Oncology (ASCO)

ISSN (Electronic): 2473-4276

Publication date Created: August 2024

Publication date (Print): August 2024

Issue: 8

Affiliations

[1 ]National Institutes of Health, All of Us Research Program, Bethesda, MD

[2 ]Leidos, Frederick, MD

[3 ]Vanderbilt University Medical Center, Nashville, TN

[4 ]InfoPro Systems, Rockville, MD

Article

DOI: 10.1200/CCI.24.00052

PubMed ID: 39178364

SO-VID: 209a207e-ebe1-4542-954f-d9b9ae6bdfcb

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