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，在组织内部和组织之间作为具有不同患者标识符的独立岛屿。对于在可能存在多个标识符的机构内收集的数据，以及对于在不同的健康护理机构、不同的药房系统、不同的付款人、不同的公共卫生机构等处收集的关于同一患者的数据，情况就是这样。这种情况阻碍了跨这些数据库聚合关于个人的信息，如临床决策支持、临床护理、公共卫生报告、临床研究和结果管理。汇总不仅对确定患者的医疗状况很重要，而且对临床有效性研究、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很重要。虽然HIE的是一个越来越普遍的来源，全面的临床，正式 缺乏明确解决HIE数据汇总方法的建议。因此，HIE目前使用各种不同的数据聚合方法。由于HIE代表了具有不同数据质量和特征的异构临床信息源的复杂“熔炉”，因此它们呈现了独特的数据聚合挑战和机遇。因此，明确的文件和传播具体的，真实的方法，准确，有效的数据汇总是至关重要的发展一个强大的和可靠的国家卫生信息网络（NHIN）。我们将正式记录和传播两种不同的、现有的联系方法，这些方法目前用于长期的、业务性的卫生信息交流。我们将实现和评估扩展的概率方法，旨在提高算法的准确性。延期将包括：参数估计方法的随机和封闭形式的解决方案;概率方法的推广，以适应统计领域之间的依赖关系;评价新的接近比较器和连续和离散的修改，允许正式列入比较器。我们将评估和扩展用于创建密切反映基础统计特征的合成连锁数据的方法。我们将评估检测特定数据特征的存在或不存在的方法，这些数据特征通知对底层概率匹配模型的扩展的选择。我们将开发和评估用于识别未通过统计独立性测试的数据元素组合的流程。我们将评估和 使用确定性和概率性方法，表征链接各种场景的真实的世界HIE数据源的技术性能和临床及操作价值。

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.