在多用途临床注册中实现一种新的基于质量改进的数据质量监测和增强方法。

Jesse Pratt, Daniel Jeffers, Eileen C King, Michael D Kappelman, Jennifer Collins, Peter Margolis, Howard Baron, Julie A Bass, Mikelle D Bassett, Genie L Beasley, Keith J Benkov, Jeffrey A Bornstein, José M Cabrera, Wallace Crandall, Liz D Dancel, Monica P Garin-Laflam, John E Grunow, Barry Z Hirsch, Edward Hoffenberg, Esther Israel, Traci W Jester, Fevronia Kiparissi, Arathi Lakhole, Sameer P Lapsia, Phillip Minar, Fernando A Navarro, Haley Neef, K T Park, Dinesh S Pashankar, Ashish S Patel, Victor M Pineiro, Charles M Samson, Kelly C Sandberg, Steven J Steiner, Jennifer A Strople, Boris Sudel, Jillian S Sullivan, David L Suskind, Vikas Uppal, Prateek D Wali
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引用次数: 0

摘要

目的:实施一个基于质量改进的系统,以测量和提高观察性临床注册中心的数据质量,从而支持学习型医疗保健系统。数据来源:ImproveCareNow网络注册中心,截至2019年9月,该注册中心包含109个参与护理中心43305名儿童炎症性肠病(IBD)患者的314250次就诊数据。研究设计: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了绩效反馈,并制定了确定未遵循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。主要发现:在数据质量衡量标准方面存在改进模式。具有完整关键数据的就诊比例从72%增加到82%。注册患者的百分比从59%提高到83%。在另外三项衡量数据一致性和及时性的指标中,有一项将性能从42%提高到63%。由于网络文档实践和成熟度的变化,一项指标的性能下降。各护理中心的数据质量存在差异。结论:基于质量改进的数据质量监测和改进方法是可行和有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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