CRII:SCH:RUI: A Digital Identity System for Accelerating Medical Communications within Rare Disease Communities

患者身份匹配是使用一组唯一的个人信息在医疗保健数据库中定位患者的过程。罕见疾病是指在美国影响不到20万人的疾病，由于患者人数较少，与更普遍研究的医疗疾病相比，它们受到的科学和商业关注要少得多。因此，高度积极和活跃的患者社区通常围绕罕见疾病形成，为患者创建和维护患者登记，以共享有关他们病情的数据和知识，以促进疾病发现。然而，许多登记处都是一次性的解决方案，以不同的方式识别患者，这为跨多个高度集中的登记处联系患者创造了主要障碍。这个项目的目标是通过开发一个可互操作和高效的身份识别系统来支持围绕罕见疾病的必要交流，同时为计算机科学本科生提供跨学科的研究经验来满足这一需求。该项目将设计、部署和评估一个由跨数据管理系统互操作驱动的数字身份识别系统。拟议方法的目标是通过创建患者身份的唯一表示和支持平台来加速患者身份识别过程，以促进这些身份的管理和交换，以确保互操作性。该项目的技术目标分为三条主线。第一线为罕见疾病创建了标准化的数字身份模型，旨在唯一地代表患者，而不直接暴露敏感、模棱两可的患者身份信息，以显著降低患者错配率。第二个线程开发了一个罕见疾病身份识别系统(RDIS)基础设施，它将使患者身份的统一和可互操作的表示和事务能够加速临床通信。第三条线索侧重于利用第一线线索中创建的数字身份模型对区域发展信息系统进行综合评价。特别是，系统的整体有效性将根据身份收集和验证过程的简易性、系统在交易合成患者配置文件的总体和平均吞吐量方面的可扩展性以及识别患者配置文件子集所需的平均周转时间进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Patient identity matching is a process that locates a patient in a healthcare database using a unique set of personal information. Rare diseases are conditions that affect fewer than 200,000 people in the US, and due to the small patient populations, they receive significantly less scientific and commercial attention compared to more commonly studied medical conditions. As a result, highly motivated and active patient communities often form around rare diseases, creating and maintaining patient registries for patients to share data and knowledge about their conditions to promote disease discovery. However, many registries have been one-off solutions that identify patients in disparate ways, creating a major barrier to linking patients across multiple highly centralized registries. The objective of this project is to address this need by developing an interoperable and efficient identity system to support necessary communications around rare diseases, while simultaneously providing interdisciplinary research experience to computer science undergraduate students.This project will design, deploy, and evaluate a digital identity system driven by interoperability across data management systems. The goal of the proposed approach is to accelerate the patient identification process by creating unique representations of patient identities and a supporting platform that facilitate the management and exchanges of those identities to ensure interoperability. The technical aims of the project are divided into three threads. The first thread creates a standardized digital identity model for rare diseases, which aims to uniquely represent patients without directly exposing sensitive, ambiguous patient identifying information to significantly reduce patient mismatching rate. The second thread develops a Rare Disease Identity System (RDIS) infrastructure, which will enable uniform and interoperable representations and transactions of patient identity to accelerate clinical communications. The third thread focuses on integrated evaluation of RDIS with the use of the digital identity model created in the first thread. In particular, the overall effectiveness of the system will be evaluated based on the ease of identity collection and verification process, the scalability of the system in terms of the overall and average throughput of transacted synthetic patient profiles, and the average turnaround time it takes to identify a subset of patient profiles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.