Model / Framework
DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data
Authors:
Jose-Franck Diaz-Garelli ,
Clinical and Translational Science Institute, Wake Forest School of Medicine, US
Elmer V. Bernstam,
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, US
MinJae Lee,
McGovern Medical School, The University of Texas Health Science Center at Houston, US
Kevin O. Hwang,
McGovern Medical School, The University of Texas Health Science Center at Houston, US
Mohammad H. Rahbar,
McGovern Medical School, The University of Texas Health Science Center at Houston, US
Todd R. Johnson
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, US
Abstract
The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge’s main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.
How to Cite:
Diaz-Garelli J-F, Bernstam EV, Lee M, Hwang KO, Rahbar MH, Johnson TR. DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2019;7(1):32. DOI: http://doi.org/10.5334/egems.286
