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. 2019 Sep 30;7(1):51.
doi: 10.5334/egems.262.

Implementing a Novel Quality Improvement-Based Approach to Data Quality Monitoring and Enhancement in a Multipurpose Clinical Registry

Jesse Pratt  1 , Daniel Jeffers  2 , Eileen C King  3 , Michael D Kappelman  4 , Jennifer Collins  5 , Peter Margolis  3 , Howard Baron  6 , Julie A Bass  7 , Mikelle D Bassett  8 , Genie L Beasley  9 , Keith J Benkov  10 , Jeffrey A Bornstein  11 , José M Cabrera  12 , Wallace Crandall  13 , Liz D Dancel  14 , Monica P Garin-Laflam  15 , John E Grunow  16 , Barry Z Hirsch  17 , Edward Hoffenberg  18 , Esther Israel  19 , Traci W Jester  20 , Fevronia Kiparissi  21 , Arathi Lakhole  22 , Sameer P Lapsia  23 , Phillip Minar  3 , Fernando A Navarro  24 , Haley Neef  25 , K T Park  26 , Dinesh S Pashankar  27 , Ashish S Patel  28 , Victor M Pineiro  29 , Charles M Samson  30 , Kelly C Sandberg  31 , Steven J Steiner  32 , Jennifer A Strople  33 , Boris Sudel  34 , Jillian S Sullivan  35 , David L Suskind  36 , Vikas Uppal  37 , Prateek D Wali  38
Affiliations

Implementing a Novel Quality Improvement-Based Approach to Data Quality Monitoring and Enhancement in a Multipurpose Clinical Registry

Jesse Pratt et al. EGEMS (Wash DC). .

Abstract

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.

Data source: 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.

Study design: 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.

Principal findings: 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.

Keywords: Data Quality; Quality Improvement; Registry.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Figure 1
Figure 1
ImproveCareNow’s data quality key driver diagram.
Figure 2
Figure 2
Sample center level data quality report as received by participating centers monthly.
Figure 3
Figure 3
Sample exceptions report demonstrating center’s view of missing data elements.
Figure 4
Figure 4
Annotated control chart displaying change in aggregate data element completeness.
Figure 5
Figure 5
Random sample of 25 centers exposed to data quality reporting and training, showing intra-visit data completeness over time. For each center, the percentage of visits each month with complete critical data elements is indicated on the y-axis. The x-axis indicates June 2010 through June 2016.
See this image and copyright information in PMC

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