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。罕见病是指在美国影响不到20万人的疾病，由于患者人数少，与更常见的医学疾病相比，它们受到的科学和商业关注明显较少。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，以分享有关其病情的数据和知识，以促进疾病发现。然而，许多登记中心都是一次性的解决方案，以不同的方式识别患者，这对将患者与多个高度集中的登记中心联系起来造成了重大障碍。本项目的目标是通过开发一个可互操作和高效的身份系统来满足这一需求，以支持围绕罕见疾病的必要通信，同时为计算机科学本科生提供跨学科的研究经验。本项目将设计，部署和评估一个由跨数据管理系统的互操作性驱动的数字身份系统。所提出的方法的目标是通过创建患者身份的唯一表示和促进这些身份的管理和交换以确保互操作性的支持平台来加速患者识别过程。该项目的技术目标分为三个方面。第一个线程为罕见病创建了一个标准化的数字身份模型，旨在唯一地表示患者，而不会直接暴露敏感、模糊的患者识别信息，以显著降低患者的错配率。第二个线程开发了一个罕见疾病身份系统（RDIS）基础设施，该基础设施将实现患者身份的统一和可互操作的表示和交易，以加速临床通信。第三个线程的重点是综合评估的RDIS与使用的数字身份模型中创建的第一个线程。具体而言，将基于身份收集和验证过程的简易性、系统在交易的合成患者简档的总体和平均吞吐量方面的可扩展性，以及识别患者资料子集所需的平均周转时间。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

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.