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

Vikas Uppal,Victor M Pineiro ,Jennifer Strople ,Julie Bass ,Prateek Wali ,Liz Dancel ,Fernando A Navarro ,Michael D Kappelman ,Arathi Lakhole,Peter A Margolis ,José Cabrera ,Keith Benkov ,Esther J Israel ,Eileen King ,Charles M Samson ,Sameer Lapsia ,H Neef ,Kelly C Sandberg ,Steven J Steiner ,Kt Park ,Traci Jester ,Ashish Patel ,Phillip Minar,Dinesh S Pashankar ,Edward J Hoffenberg ,Barry Z Hirsch ,Daniel Jeffers,Wallace Crandall,John E Grunow ,Jillian S Sullivan ,Jesse Pratt,Jennifer Collins,Boris Sudel,David L Suskind ,F Kiparissi ,Monica P Garin-Laflam ,Mikelle Bassett ,Jeffrey A Bornstein ,Genie Beasley ,Howard Baron

doi:10.5334/egems.262

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.

Highlights

There is growing interest in the potential for clinical registries that can simultaneously support clinical care, quality improvement (QI), and research
The percentage of registered patients increased from 59 percent to 83 percent, with 51 percent of centers having registered at least 90 percent of their Inflammatory Bowel Disease (IBD) population
We found that efforts to improve data quality that included training in quality improvement methods, and tools including control charts, exception reports and failure mode and effect analysis resulted in improvement in a range of data quality measures

Summary

Introduction

There is growing interest in the potential for clinical registries that can simultaneously support clinical care, quality improvement (QI), and research. Some multi-center networks are beginning to adopt principles of open science, or network-based production [4], to foster collaborative improvement, research, data sharing, and innovation. In this setting, the registry functions to provide access to condition-specific information in a uniform way to support clinical care and to support QI and research to improve patient outcomes

Methods

Results

Conclusion