Advancing Patient Identity Management in the Context of Real-World Health Informa

描述(由申请人提供)：医疗保健信息越来越多地分布在许多独立的数据库和系统中，无论是在组织内部还是组织之间，都是具有不同患者识别符的独立孤岛。对于在可能有多个标识符的机构内收集的数据，以及在不同的医疗保健机构、不同的药房系统、不同的付款人、不同的公共卫生机构等收集的关于同一患者的数据，情况就是如此。这种情况阻碍了在临床决策支持、临床护理、公共卫生报告、临床研究和结果管理所需的数据库中收集关于个人的信息。汇总不仅对确定患者的医疗保健状态很重要，而且对于临床有效性研究、药物安全性研究和其他需要综合数据的基于人群的研究也是重要的。虽然缺氧缺血性脑病是一种日益常见的综合临床、正规 缺乏明确处理HIE数据聚合方法的建议。因此，HIE目前使用各种不同的数据聚合方法。由于HIE代表着具有不同数据质量和特征的不同临床信息源的复杂“熔炉”，它们提出了独特的数据聚合挑战和机会。因此，清晰的文件记录和传播具体的、真实的方法，以实现准确、高效和数据聚合，对于发展强大和可靠的国家卫生信息网(NHIN)至关重要。我们将正式记录和传播目前在长期的、可操作的卫生信息交流中使用的两类不同的、现有的联系方法。我们将实施和评估概率方法的扩展，这些扩展旨在提高算法的准确性。扩展将包括：参数估计方法的随机和封闭形式的解；为适应场之间的统计相关性而推广的概率方法；评价新的近似度比较器以及允许正式列入比较器的连续和离散修改。我们将评估和推广创建密切反映基础统计特征的合成关联数据的方法。我们将评估检测特定数据特征的存在或不存在的方法，这些特征为选择底层概率匹配模型的扩展提供信息。我们将开发和评估识别未通过统计独立性测试的数据元素组合的过程。我们将评估和 描述使用确定性和概率性方法为各种场景链接真实HIE数据源的技术性能以及临床和操作价值。

DESCRIPTION (provided by applicant): Healthcare information is increasingly distributed across many independent databases and systems, both within and among organizations as separate islands with different patient identifiers. This is the case for data collected within an institution where there may be multiple identifiers, and for data collected about the same patient at different health care institutions, different pharmacy systems, different payers, different public health agencies, and so on. This situation hinders the aggregation of information about individuals across such databases as needed for clinical decision support, clinical care, public health reporting, clinical research, and outcomes management. Aggregation is important not only to determine a patient's health care status, but also for clinical effectiveness research, drug safety research and other population-based studies requiring comprehensive data. While HIE's are an increasingly common source of comprehensive clinical, formal recommendations explicitly addressing HIE data aggregation approaches are lacking. Consequently, HIE's currently use a variety of differing data aggregation approaches. Because HIE's represent complex "melting pots" of heterogeneous clinical information sources with varying data quality and characteristics, they present unique data aggregation challenges and opportunities. Therefore, clear documentation and dissemination of concrete, real-world methods for accurate, efficient, and data aggregation are crucial to developing a robust and reliable National Health Information Network (NHIN). We will formally document and disseminate two distinct, existing classes of linkage methodologies currently used in the context of a long-standing, operational health information exchange. We will implement and evaluate extensions to the probabilistic method that are designed to improve algorithm accuracy. Extensions will include: stochastic and closed-form solutions for parameter estimation methods; generalization of the probabilistic method to accommodate statistical dependence between fields; evaluation of novel nearness comparators and continuous and discrete modifications allowing formal inclusion of comparators. We will evaluate and extend methods for creating synthetic linkage data that closely reflects the statistical characteristics of the underlying. We will evaluate methods that detect the presence or absence of specific data characteristics that inform the selection of extensions to the underlying probabilistic matching model. We will develop and evaluate processes for identifying data element combinations that fail the test for statistical independence. We will evaluate and characterize the technical performance and clinical and operational value of linking real world HIE data sources for a variety of scenarios using both deterministic and probabilistic methods.