一个从多个机构调整数据进行有意义分析的框架。

Jay Knowlton, Tom Belnap, Bonnie Patelesio, Elisa L Priest, Friedrich von Recklinghausen, Andreas H Taenzer
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引用次数: 4

摘要

导言:卫生系统可以通过注重数据共享和比较分析的协作网络得到支持,以确定和迅速传播有前途的护理做法。标准化的数据收集、质量评估和清理是促进操作、质量改进和研究的有意义分析的必要过程。我们开发了一个框架,用于使用高价值医疗保健协作中心注册中心来校准来自医疗保健提供系统的数据。框架:集中式数据注册中心模型允许多层数据质量评估。我们的框架使用迭代方法,从清晰的规范开始,与不同的涉众保持持续的对话,并定期检查以评估数据的一致性、完整性和合理性。经验教训:我们发现迭代通信过程对于中央注册中心至关重要,以确保:1)数据规范的清晰度,2)适当的数据质量,以及3)对数据源、目的和上下文的彻底理解。让所有参与机构的团队参与,并在所有决策过程中纳入临床医生、信息技术专家、数据分析师、运营经理和卫生服务研究人员等不同利益相关者,支持开发高质量的数据集,用于跨多个机构的比较分析。结论:标准的数据规范和提交过程本身并不能保证协作注册中心的数据一致。实现具有广泛沟通的迭代数据质量改进框架被证明是有效的,可以将来自多个机构的数据对齐以支持有意义的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Framework for Aligning Data from Multiple Institutions to Conduct Meaningful Analytics.

A Framework for Aligning Data from Multiple Institutions to Conduct Meaningful Analytics.

A Framework for Aligning Data from Multiple Institutions to Conduct Meaningful Analytics.

Introduction: Health systems can be supported by collaborative networks focused on data sharing and comparative analytics to identify and rapidly disseminate promising care practices. Standardized data collection, quality assessment, and cleansing is a necessary process to facilitate meaningful analytics for operations, quality improvement, and research. We developed a framework for aligning data from health care delivery systems using the High Value Healthcare Collaborative central registry.

Framework: The centralized data registry model allows for multiple layers of data quality assessment. Our framework uses an iterative approach, starting with clear specifications, maintaining ongoing dialogue with diverse stakeholders, and regular checkpoints to assess data conformance, completeness, and plausibility.

Lessons learned: We found that an iterative communication process is critical for a central registry to ensure: 1) clarity of data specifications, 2) appropriate data quality, and 3) thorough understanding of data source, purpose, and context. Engaging teams from all participating institutions and incorporating diverse stakeholders of clinicians, information technologists, data analysts, operations managers, and health services researchers in all decision making processes supports development of high quality datasets for comparative analytics across multiple institutions.

Conclusion: A standard data specification and submission process alone does not guarantee aligned data for a collaborative registry. Implementing an iterative data quality improvement framework with extensive communication proved to be effective for aligning data from multiple institutions to support meaningful analytics.

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