RAPID: Information Retrieval and Graph Mining Techniques to Enable Self-Assessment and Clinical Monitoring of Emergent Infectious Diseases

该项目将开发一个系统，使罗德岛州的患者能够进入并监测与新冠肺炎相关的可能症状。该系统还将使患者能够将自己的症状与该州的其他症状进行比较。该系统最终将为个人是否应该(1)立即寻求临床评估；(2)在家中管理症状；或(3)继续监测症状提供自动指导。该系统将是一个门户网站的一部分，该门户网站使患者可以从罗德岛医疗保健系统的医疗提供者那里获得临床数据。该系统的数据将被用于开发计算机模型，这些模型可以用来更好地预测未来潜在的新冠肺炎疫情。由此产生的系统和计算机模型将被设计成更好地预测未来其他潜在的传染病爆发。这个快速项目将侧重于数据科学和信息学方法的发展和进步，以建立一个急需的基础设施，以支持人口水平的综合症监测，最初的重点是支持患者查询和管理医疗保健系统上的患者负载。具体地说，该项目的目标是开发一个基于患者报告和电子健康记录数据的实时监测系统，以促进对新冠肺炎等新出现流行病的公共卫生应对。具体地说，该团队将：(1)创建一个以患者为中心的系统，更好地告知适当的医疗服务寻求；以及(2)开发一个用于人口级临床决策支持和监测的平台，该平台使用来自罗德岛州各地医疗保健提供者的数据，使用EHR系统。该系统将提供与健康状况的对应关系，可与指导后续建议的资源相联系。将利用焦点小组来探索面向患者的制度的可接受性和可行性。然后，将从入院、出院和转院的数据以及临床和实验室数据的组合中生成概率症状-状况模型，这些数据将用于指导对感染风险最高的个人发出有针对性的警报。该模型还将包括时间信息，以便能够对进展率和承认诊断的时间进行建模。该模型的有效性将由一个临床医生团队进行评估。该模型将用于确定疾病发展模式，以及确定公共卫生干预的目标，如地理热点或其他高危社区。该项目的成功有可能在罗德岛州对新冠肺炎的响应方面产生立竿见影的影响，并有可能通过使用EHR在全国范围内为疾病监测流程提供信息。该奖项反映了美国国家科学基金会的法定使命，并已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。

This project will develop a system for enabling patients in Rhode Island to enter and monitor possible symptoms associated with COVID-19. The system will also enable patients to compare their symptoms to others in the state. The system will eventually provide automated guidance on whether and individual should (1) seek immediate clinical assessment; (2) manage symptoms at home; or (3) continue monitoring symptoms. The system will be part of a web portal that makes clinical data available to patients from medical providers across the Rhode Island healthcare system. Data from the system will be used to develop computer models that can be used to better predict potential subsequent COVID-19 outbreaks. The resulting system and computer models will be designed to better predict other potential infectious disease outbreaks in the future. This RAPID project will focus on the development and advancement of data science and informatics approaches to establish a much-needed infrastructure for supporting population-level syndromic monitoring, with an initial emphasis on supporting patient inquiry and managing patient loads on the healthcare system. Specifically, the goal of this project is to develop a real-time monitoring system based on patient-reported and electronic health record (EHR) data to facilitate public health responses to emerging epidemics like COVID-19. Specifically, the team will: (1) Create a patient-centered system that better informs appropriate seeking of healthcare services; and (2) Develop a platform for population-level clinical decision support and monitoring that uses data from healthcare providers across Rhode Island using an EHR system. The system will provide an alignment to health conditions that can be linked to resources to guide follow up recommendations. Focus groups will be used to explore acceptability and feasibility of the patient-facing system. A probabilistic symptom-condition model will then be generated from a combination of data from admissions, discharges and transfers, as well as clinical and laboratory data, which will be used to guide targeted alerts to individuals at highest risk of infection. This model will also include temporal information to enable modeling of the rate of progression and time to admitting diagnosis. The validity of the model will be assessed by a team of clinicians. The model will be used to identify patterns of disease progression, as well as identify targets for public health intervention such as geographical hotspots or other at-risk communities. The success of this project has the potential to have immediate impact in Rhode Island in its response to COVID-19, and potentially inform disease monitoring processes through the use of EHRs nationally.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.