Building Patient-Centered Outcomes Research Value and Integrity with Data Quality and Transparency Standards [Methods Study], United States, 2013 - 2018 (ICPSR 39529)
Version Date: Oct 22, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Michael G. Kahn, University of Colorado Anschutz Medical Campus
https://doi.org/10.3886/ICPSR39529.v1
Version V1
Alternate Title View help for Alternate Title
Summary View help for Summary
Many healthcare systems use electronic health records. Researchers use data from these records in their studies. Some records have missing or incorrect data. When this happens, people might not be able to trust a study's results. The research team wanted to:
- Create guidance to judge whether data that a study used were high quality
- Find new ways to display the quality of data
- Learn why researchers don't always report the quality of data that they used in studies
To access the methods and software, please visit the DQCODE-A-Thon GitHub.
Citation View help for Citation
Export Citation:
Funding View help for Funding
Subject Terms View help for Subject Terms
Geographic Coverage View help for Geographic Coverage
Distributor(s) View help for Distributor(s)
Study Purpose View help for Study Purpose
To create standards for evaluating and reporting data quality in electronic health records by:
- Developing data-user-driven recommendations for evaluating and reporting data quality
- Defining and assessing a common model for storing data-quality measures
- Developing data-quality reports and visuals tailored to data users
- Exploring technical, professional, and policy barriers to increasing data-quality transparency
Study Design View help for Study Design
Patient-centered outcomes research relies on the increasing availability of operational patient-specific electronic data sources, including electronic health records. Because these data sources are typically developed for purposes other than research, challenges arise when attempting to analyze and report the data. Data-quality issues prevalent in electronic health records include missing, inaccurate, and inconsistent values.
The researchers used personal contacts with healthcare and research organizations to recruit 92 participants for two study groups. One group included patients, patient advocates, and healthcare policy makers. The second group included informatics professionals, statisticians, and clinical investigators. Study participants drafted data-quality terms, categories, and definitions during face-to-face workshops, monthly webinars, and 10 presentations at professional meetings.
The researchers also collected input from another 138 data-quality researchers on drafts of the data-quality terms, categories, and definitions through an online wiki. The researchers analyzed transcripts of the meetings using iterative thematic analysis to determine consensus-based data quality standards and reporting metrics.
The researchers recruited project leaders at six large health systems to perform data-quality checks on datasets using the data-quality standards generated by the study groups. The research team held workshops for study participants to generate data codes for a common data model and to explore effective ways to display data for data users.
The researchers also distributed an anonymous online survey to 141 data users to assess professional and personal barriers to data-quality reporting.
Universe View help for Universe
Patients, patient advocates, healthcare policy makers, informatics professionals, statisticians, and clinical investigators
Data Source View help for Data Source
Transcripts from meetings with data users, including patients, patient advocates, healthcare policy makers, informatics professionals, statisticians, and clinical investigators
Notes
The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.