Enhancing Patient Matching in Support of Operational Health Information Exchange

项目摘要/摘要 许多医疗目的都需要来自广泛来源的综合医疗数据，包括 确保提供高质量的护理，并支持以患者为中心的结果研究。然而，医疗保健数据 跨多个独立系统生成，其中数据存储为具有不同患者的独立孤岛 标识符，导致患者信息碎片化和不完整。因此，有效的循证 为了最大限度地提高卫生保健数据的准确性和完整性，需要采用患者匹配方法。那里 只有有限的研究集中在患者配对方法上，而且很少有正式的， 使用真实世界对基于共识的匹配策略建议进行全面评估， 不同的医疗保健数据。跨多个系统的临床数据的“拼凑被子”集合是 越来越普遍的和先前的配对研究未能反映这些数据面临的挑战。因此，评估 针对现实世界、稳健、准确的患者配对方法的最佳实践建议在 健康信息交换和其他新兴的大型医疗保健数据来源所反映的背景是 有必要提供证据，为患者配对提供新的最佳实践建议。而当 具有丰富操作经验的主题专家通知了最近的建议，有 目前有不完整的同行评议的证据基础，以充分支持最近 指导。在没有进一步的正式评估以加强和完善这些建议的情况下，组织可以 不要那么倾向于追求改进，否则他们可能会实施几乎没有什么好处的方法。我们的长期目标是 确保可持续的学习保健系统基础设施，其中包括准确、一致和 高效的患者身份管理。实现这一目标的下一步是为目前最低限度的 患者主体匹配证据，为流程、政策讨论和支持的技术提供信息 一致、准确和高效的患者身份管理方法。要解决有限的主体 对于现实世界的患者配型，我们的团队嵌入了无与伦比的体外患者配型 全国最大的健康信息交换研究实验室，其中包含数百种不同的 操作性临床数据来源。在这个实验室中，我们已经实施、评估和部署了 以及改进患者匹配的实用方法，这些方法已经改善了许多特定的现实世界的临床、公共 健康和研究过程。因此，我们处于有利地位，可以评估出现的影响 基于共识的最佳实践建议将针对提高质量、标准化和 在广泛的常规医疗保健环境中收集的数据的辨别能力。我们将进一步评估 在相同情况下优化匹配方法的性能。这样的证据可以提供有意义的信息 下一步制定全国患者身份管理战略。

Project Summary/Abstract Integrated health care data from a broad set of sources is required for many health care purposes including assuring high quality care delivery and enabling patient-centered outcomes research. However, health care data is generated across many independent systems where data is stored as separate islands with different patient identifiers, resulting in fragmented and incomplete patient information. Therefore, effective evidence-based patient matching methods are needed to maximize the accuracy and completeness of health care data. There is a limited body of research focused on patient matching methods and there have been few formal, comprehensive evaluations of consensus-based matching strategy recommendations using real-world, heterogeneous health care data. The “patchwork quilt” collections of clinical data spanning multiple systems are increasingly common and prior matching studies fail to reflect challenges faced by these data. Thus, evaluating the performance of best-practice recommendations for real-world, robust, accurate patient matching methods in contexts reflected by health information exchanges and other emerging large health care data sources is necessary to provide evidence informing emerging best-practice recommendations for patient matching. While subject matter experts with substantial operational experience informed recent recommendations, there is currently an incomplete peer-reviewed evidence base to fully support the feasibility and effectiveness of recent guidance. Without further formal evaluation to strengthen and refine these recommendations, organizations may be less inclined to pursue improvements or they may implement methods of little benefit. Our long-term goal is to ensure a sustainable learning health care system infrastructure, which includes accurate, consistent and efficient patient identity management. The next step in achieving that goal is to contribute to the current minimal body of patient matching evidence to inform processes, policy discussion, and technology that support consistent, accurate, and efficient patient identity management methods. To address the limited body of knowledge for real-world patient matching, our team has embedded an unparalleled in-vitro patient matching research laboratory in the nation’s largest health information exchange, which contains hundreds of diverse operational clinical data sources. Within this laboratory we have implemented, evaluated and deployed novel and practical methods for improving patient matching that have improved many specific real world clinical, public health, and research processes. Consequently, we are well positioned to evaluate the impact that emerging consensus-based best practice recommendations will have on improving the quality, standardization, and discriminating power of data collected in a broad set of routine health care settings. We will further evaluate the performance of optimized matching methodologies in the same context. Such evidence can meaningfully inform next steps in the formulation of the nationwide patient identity management strategy.