Pratt Jesse, Jeffers Daniel, King Eileen C, Kappelman Michael D, Collins Jennifer, Margolis Peter, Baron Howard, Bass Julie A, Bassett Mikelle D, Beasley Genie L, Benkov Keith J, Bornstein Jeffrey A, Cabrera José M, Crandall Wallace, Dancel Liz D, Garin-Laflam Monica P, Grunow John E, Hirsch Barry Z, Hoffenberg Edward, Israel Esther, Jester Traci W, Kiparissi Fevronia, Lakhole Arathi, Lapsia Sameer P, Minar Phillip, Navarro Fernando A, Neef Haley, Park K T, Pashankar Dinesh S, Patel Ashish S, Pineiro Victor M, Samson Charles M, Sandberg Kelly C, Steiner Steven J, Strople Jennifer A, Sudel Boris, Sullivan Jillian S, Suskind David L, Uppal Vikas, Wali Prateek D
Pharmaceutical Product Development, US.
Total Quality Logistics, US.
EGEMS (Wash DC). 2019 Sep 30;7(1):51. doi: 10.5334/egems.262.
To implement a quality improvement based system to measure and improve data quality in an observational clinical registry to support a Learning Healthcare System.
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.
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.
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.
A quality improvement based approach to data quality monitoring and improvement is feasible and effective.
实施一个基于质量改进的系统，以测量和提高观察性临床登记处的数据质量，以支持学习型医疗系统。
ImproveCareNow网络登记处，截至2019年9月，该登记处包含来自109个参与护理中心的43,305名儿童炎症性肠病（IBD）患者的314,250次就诊数据。
使用统计过程控制方法评估数据质量改进支持对护理中心的影响。定义了数据质量指标，使用统计过程控制图对这些指标进行性能反馈，并编制了识别未通过数据质量检查的数据项的报告，以使各中心能够监测和提高其数据质量。
数据质量指标呈现出改进的趋势。具有完整关键数据的就诊比例从72%提高到了82%。注册患者的比例从59%提高到了83%。在另外三个数据一致性和及时性指标中，有一个指标的性能从42%提高到了63%。由于网络文档实践的变化和成熟，有一个指标的性能下降。各护理中心的数据质量存在差异。
基于质量改进的数据质量监测和改进方法是可行且有效的。
