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，都是如此。这种情况阻碍了临床决策支持、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个人信息的聚合。汇总不仅对确定患者的卫生保健状况很重要，而且对临床有效性研究、药物安全性研究和其他需要全面数据的基于人群的研究也很重要。虽然HIE是越来越普遍的综合临床来源，正式的

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.