Abstract
To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
