Data Use and Quality, Informatics, Electronic Health Record (EHR), Common Data Model
Introduction: Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community.
Methods: We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations.
Results: We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation.
Discussion: Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.
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Huser, Vojtech; DeFalco, Frank J.; Schuemie, Martijn; Ryan, Patrick B.; Shang, Ning; Velez, Mark; Park, Rae Woong; Boyce, Richard D.; Duke, Jon; Khare, Ritu; Utidjian, Levon; and Bailey, Charles
"Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Datasets,"
eGEMs (Generating Evidence & Methods to improve patient outcomes):
1, Article 24.
Available at: http://repository.edm-forum.org/egems/vol4/iss1/24