Enhancing Patient Matching in Support of Operational Health Information Exchange

项目总结/摘要 许多医疗保健目的都需要来自广泛来源的综合医疗保健数据，包括 确保高质量的医疗服务，并实现以患者为中心的结果研究。然而，医疗保健数据 在许多独立的系统中生成，其中数据存储为不同患者的独立岛屿 标识符，导致患者信息碎片化和不完整。因此，有效的循证 需要患者匹配方法来最大化健康护理数据的准确性和完整性。那里 是一个有限的研究机构，专注于病人匹配方法， 使用真实世界的基于共识的匹配策略建议的综合评估， 异构医疗保健数据。跨多个系统的临床数据的“拼凑被子”集合是 越来越常见的和先前的匹配研究未能反映这些数据所面临的挑战。因此，评估 最佳实践建议在以下方面的表现：真实世界、稳健、准确的患者匹配方法 卫生信息交换和其他新兴的大型卫生保健数据源所反映的背景， 有必要提供证据，为患者匹配的新兴最佳实践建议提供信息。而 具有丰富业务经验的专题专家最近提出了建议， 目前，同行评审的证据基础不完整，无法充分支持最近 指导如果没有进一步的正式评价来加强和完善这些建议，各组织可能 不太倾向于追求改进，或者他们可能会实施效益不大的方法。我们的长期目标是 确保可持续的学习医疗保健系统基础设施，其中包括准确、一致和 有效的患者身份管理。实现这一目标的下一步是为目前最低限度的 患者匹配证据的主体，以告知支持的流程、政策讨论和技术 一致、准确和高效的患者身份管理方法。为了解决有限的机构， 我们的团队已经嵌入了无与伦比的体外患者匹配， 研究实验室在全国最大的健康信息交换，其中包含数百种不同的 操作临床数据源。在这个实验室里，我们实施、评估和部署了新的 以及用于改善患者匹配的实用方法，这些方法已经改善了许多特定的真实的世界临床、公共 健康和研究过程。因此，我们有能力评估正在出现的影响， 基于共识的最佳实践建议将对提高质量，标准化， 在广泛的常规卫生保健环境中收集的数据的鉴别力。我们将进一步评估 优化匹配方法在相同背景下的性能。这些证据可以有意义地告知 制定全国患者身份管理战略的下一步工作。

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.