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.
