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Empirical research

Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions

Authors:

Tiffany J. Callahan ,

Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus
About Tiffany J.
MPH
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Juliana G. Barnard,

Adult and Child Consortium for Health Outcomes and Research Delivery Science, University of Colorado Anschutz Medical Campus
About Juliana G.
MA
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Laura J. Helmkamp,

Adult and Child Consortium for Health Outcomes and Research Delivery Science, University of Colorado Anschutz Medical Campus
About Laura J.
MS
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Julie A. Maertens,

Adult and Child Consortium for Health Outcomes and Research Delivery Science, University of Colorado Anschutz Medical Campus
About Julie A.
PhD
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Michael G. Kahn

Department of Pediatrics, School of Medicine, University of Colorado Denver Anschutz Medical Campus
About Michael G.
MD, PhD
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Abstract

Introduction: Electronic health record (EHR) data are known to have significant data quality issues, yet the practice and frequency of assessing EHR data is unknown. We sought to understand current practices and attitudes towards reporting data quality assessment (DQA) results by data professionals.

Methods: The project was conducted in four Phases: (1) examined current DQA practices among informatics/CER stakeholders via engagement meeting (07/2014); (2) characterized organizations conducting DQA by interviewing key personnel and data management professionals (07-08/2014); (3) developed and administered an anonymous survey to data professionals (03-06/2015); and (4) validated survey results during a follow-up informatics/CER stakeholder engagement meeting (06/2016).

Results: The first engagement meeting identified the theme of unintended consequences as a primary barrier to DQA. Interviewees were predominantly medical groups serving distributed networks with formalized DQAs. Consistent with the interviews, most survey (N=111) respondents utilized DQA processes/programs. A lack of resources and clear definitions of how to judge the quality of a dataset were the most commonly cited individual barriers. Vague quality action plans/expectations and data owners not trained in problem identification and problem-solving skills were the most commonly cited organizational barriers. Solutions included allocating resources for DQA, establishing standards and guidelines, and changing organizational culture.

Discussion: Several barriers affecting DQA and reporting were identified. Community alignment towards systematic DQA and reporting is needed to overcome these barriers.

Conclusion: Understanding barriers and solutions to DQA reporting is vital for establishing trust in the secondary use of EHR data for quality improvement and the pursuit of personalized medicine.

How to Cite: Callahan TJ, Barnard JG, Helmkamp LJ, Maertens JA, Kahn MG. Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2017;5(1):16. DOI: http://doi.org/10.13063/egems.1297
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Published on 04 Sep 2017.
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