Description
Ensuring the quality of clinical data is critical to the mission of building actionable knowledge from healthcare data. However, our understanding of the impact of clinical data quality is still in it’s infancy. During the AMIA Annual Symposium in 2018 informaticians came together in a CRI working group workshop to discuss the impact of data quality on clinical research and set a research agenda for data quality. The AMIA fall meeting brought distributed communities together to share best practices and knowledge. In this workshop we worked collaboratively to ascertain the importance of data quality as well as aspects of data quality assessments such as the business case for data quality and driving data quality assessment results into practice and publications. These discussions touched on many important factors in addition to those planned for the workshop. This follow-up workshop in 2019 will share the results of the previous meeting and to expand on topics that arose during that initial conversation.
Along with a discussion and vetting of research agenda componenta synthesized last year at the workshop, our expanded discussion will include data quality and 1) its effect on applications, 2) integration of clinical data, 3) interoperability. An understanding of the impact of data quality on many applications is critical; for example, privacy-protecting and secure deduplication processes are complicated by poor data quality. Also, the quality of data directly influences integration of clinical data with biobank data, genomic data, and open source registries. A deeper understanding of this relationship can be gleaned from sharing successes and failures as a community. Finally, interoperability is a growing concern in relation to data quality. A discussion of CRI gaps and challenges will provide not only a forum for knowledge sharing but a basis for future research and publication.
Learning Objective: 1. Understand the significance of ROI determination in research data quality and building actionable knowledge from data
2. Learn about key factors in the DQA process from testing to practice and publication
3. Understand current data quality issues that impact clinical research informatics and get current on data quality assessment literature.
4. Become aware of research opportunities in areas where DQ effects clinical research
Authors:
Melody Penning (Presenter)
University of Arkansas for Medical Sciences
Vojtech Huser (Presenter)
National Library of Medicine
Catherine Craven (Presenter)
Mount Sinai Health System
Karthik Natarajan (Presenter)
Columbia University
Abu Mosa (Presenter)
University of Missouri School of Medicine
Presentation Materials: