Dissertation/Thesis Abstract

3 × 3 Data Quality Assessment Tool Analysis for Electronic Health Records’ Secondary Use
by Suler, Dennis J., Jr., D.B.A., Capella University, 2019, 135; 22621959
Abstract (Summary)

This Delphi study includes an analysis of 3 × 3 data quality assessment (DQA), an electronic health records (EHR) DQA framework used for assessing the quality of EHR for use in secondary research. While the 3 × 3 DQA was developed and its creators received feedback, the level of feedback lacked numerous elements. This Delphi study includes a review of the 3 × 3 DQA as well as data quality assessments in the secondary use of EHR systems for research, examining how a data quality assessment tool creates a complete and accurate format for performing data quality assessments of EHR systems. The Delphi study includes a determination of how the implementation of a data quality assessment tool increases the quality and completeness of EHR data and an examination of how a data quality assessment tool improved the quality of clinical outcomes and research. Research was performed via a qualitative Delphi study, in which participants were asked to answer two rounds of questions. Each round contained a mixture of closed- and open-ended questions, and responses from the first round created the second round of questions. A panel of expert participants in the secondary use of EHR data who were published in at least one peer-reviewed journal was selected. Participants were located around the world, with most participants publishing peer-reviewed articles within the previous year. Although the 3 × 3 DQA was an easy-to-understand framework, the 3 × 3 DQA was a good starting point, particularly with more complicated EHR datasets. The Delphi study demonstrated that the DQA is one step in a larger process of data quality assessment and cleansing and highlights that practitioners must be prepared for this and build this into their processes when using EHR data. Findings included the need for standardizing EHR data fields and that concordance, the ability to match patient records across sources over time is an important often missing piece in EHR systems resulting in low data quality.

Indexing (document details)
Advisor: Morris, Johnny
Commitee: Wood, Vanessa, Butler, Cliff
School: Capella University
Department: School of Business and Technology
School Location: United States -- Minnesota
Source: DAI-A 81/4(E), Dissertation Abstracts International
Subjects: Information science, Business administration, Health care management
Keywords: 3 × 3 DQA, Data quality, Data quality assessment, Data quality assessment tool, EHR secondary use
Publication Number: 22621959
ISBN: 9781687944887
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