数据质量驱动的医疗保健改进:系统文献综述
Anthony Lighterness1, Michael Adcock1, Lauren Abigail Scanlon1
1Clinical Outcomes and Data Unit, The Christie NHS Foundation Trust, Manchester, United Kingdom.
Journal of medical Internet research
|August 22, 2024
概括
高质量的数据对于现实世界的证据和学习卫生系统至关重要. 本次审查强调了对评估和改善医疗保健研究数据质量 (DQ) 的标准化方法的需求.
科学领域:
- 医疗信息学 医疗信息学
- 数据质量管理数据质量管理
- 现实世界的证据.
背景情况:
- 高质量的数据对于推进现实世界的证据和学习医疗保健系统至关重要.
- 尽管已认识到数据质量 (DQ) 的问题,但评估和改进的标准化最佳实践仍然难以捉摸.
研究的目的:
- 系统地审查研究研究如何定义,评估和提高结构化的真实世界医疗保健数据的质量.
- 确定用于数据质量改进倡议的当前方法和框架.
主要方法:
- 进行了Embase和PubMed (1945-2023) 的系统文献搜索,以研究结构化现实世界数据质量.
- 使用数据管理协会 (DAMA) DQ框架标准化数据质量概念.
- 在两位独立作者进行选后,确定了39篇相关文章,报告了数据质量改善举措.
主要成果:
- 在研究环境,DQ评估方法 (主要是手动的,重点是完整性) 和使用的DQ框架中观察到显著的异质性.
- 对质量改进的标准化方法的有限使用,计划-做-研究-行动 (PDSA) 和定义-测量-分析-改进-控制 (DMAIC) 周期是常见的.
- 最有效的DQ改进是由于综合干预,包括反,IT解决方案,培训,工作流程改进和数据清理.
结论:
- 在数据质量改进研究中,迫切需要标准化指导方针,以便合成和比较研究结果.
- 像PDSA和DAMA这样的框架,以及根源原因分析,可以指导可持续的DQ改进.
- 标准化DQ评估,分析和改进方法对于提高研究的有效性,可比性和通用性至关重要.
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