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Implementing a Novel Quality Improvement-Based Approach to Data Quality Monitoring and Enhancement in a Multipurpose Clinical Registry. 2019
Jesse Pratt, and
Daniel Jeffers, and
Eileen C King, and
Michael D Kappelman, and
Jennifer Collins, and
Peter Margolis, and
Howard Baron, and
Julie A Bass, and
Mikelle D Bassett, and
Genie L Beasley, and
Keith J Benkov, and
Jeffrey A Bornstein, and
José M Cabrera, and
Wallace Crandall, and
Liz D Dancel, and
Monica P Garin-Laflam, and
John E Grunow, and
Barry Z Hirsch, and
Edward Hoffenberg, and
Esther Israel, and
Traci W Jester, and
Fevronia Kiparissi, and
Arathi Lakhole, and
Sameer P Lapsia, and
Phillip Minar, and
Fernando A Navarro, and
Haley Neef, and
K T Park, and
Dinesh S Pashankar, and
Ashish S Patel, and
Victor M Pineiro, and
Charles M Samson, and
Kelly C Sandberg, and
Steven J Steiner, and
Jennifer A Strople, and
Boris Sudel, and
Jillian S Sullivan, and
David L Suskind, and
Vikas Uppal, and
Prateek D Wali
Pharmaceutical Product Development, US.
OBJECTIVE To implement a quality improvement based system to measure and improve data quality in an observational clinical registry to support a Learning Healthcare System. METHODS ImproveCareNow Network registry, which as of September 2019 contained data from 314,250 visits of 43,305 pediatric Inflammatory Bowel Disease (IBD) patients at 109 participating care centers. METHODS The impact of data quality improvement support to care centers was evaluated using statistical process control methodology. Data quality measures were defined, performance feedback of those measures using statistical process control charts was implemented, and reports that identified data items not following data quality checks were developed to enable centers to monitor and improve the quality of their data. RESULTS There was a pattern of improvement across measures of data quality. The proportion of visits with complete critical data increased from 72 percent to 82 percent. The percent of registered patients improved from 59 percent to 83 percent. Of three additional measures of data consistency and timeliness, one improved performance from 42 percent to 63 percent. Performance declined on one measure due to changes in network documentation practices and maturation. There was variation among care centers in data quality. CONCLUSIONS A quality improvement based approach to data quality monitoring and improvement is feasible and effective.
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