# Assisted living communities- transforming predictive data into proactive care for COVID-19

> **NIH NIH P30** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $371,423

## Abstract

ABSTRACT
Recent data suggests that that older Americans who contact COVID-19 are at greatest risk for hospitalization and
poor outcomes. Additionally, due to advanced age and their high likelihood of having multiple chronic conditions,
adults in senior living facilities are at highest risk for developing COVID-19, its most serious complications, and
dying. Since the identification of first US case of novel coronavirus 2019 disease (COVID-19) in the Seattle,
Washington, several outbreaks have been identified in long-term care and assisted living facilities with evidence of
rapid spread. Older residents and the staff of long-term care assisted living facilities as well as public health
officials are facing a multitude of challenges which render early detection of COVID-19 infections difficult in
these facilities and which have posed a major barrier to the efforts to control the spread of infection. Adding to
these challenges, more than half of residents with positive COVID-19 test results are asymptomatic at the time
of testing, further contributing to transmission. There is an urgent unmet need for strategies for monitoring
of residents in long-term care and assisted living facilities to facilitate early detection of the infection
using means that require minimal person-to-person contact.
While the dynamics of COVID-19 infection spread is being addressed by several contact tracing apps,
assessing the risk for development of severe symptoms and hospitalization in these community residents
requires active physiological monitoring and ecological momentary assessment in the context of preexisting
clinical conditions and presents an immediate unmet need. With this project, we propose to deliver a user-
friendly COVID-19 early detection alert platform (COVID-Alert) that integrates: 1) biosensor ensemble that
noninvasively and continuously monitor and record critical vital signs (temperature, heart rate, respiratory rate,
oxygen saturation, and activity level); 2) ecological momentary assessment (EMA) using the 5-question set
released by CDC and adopted across US by healthcare providers and health insurance industry; 3) artificial
intelligence framework that triggers an alert based on synthesis of real-time physiological biosensing data feed,
EMA monitoring of symptoms, with personalized risk profiles of preexisting conditions derived from electronic
health record maintained by the facility.
COVID-19 clinical decision support integrated into the workflow of long-term care facilities will ensure that
residents receive appropriate and timely care (resident level) and ongoing surveillance to prevent an outbreak
(facility level) while avoiding unnecessary staff exposure. This study brings together a strong interdisciplinary
team of experts in engineering, informatics, data science, machine learning, and CDS. The advanced data-driven
predictive model will be trained and validated using both high-dimensional EHR data and clinician feedback. The
process of the ...

## Key facts

- **NIH application ID:** 10165245
- **Project number:** 3P30AG031679-10S3
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** SHALENDER BHASIN
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $371,423
- **Award type:** 3
- **Project period:** 2008-09-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10165245

## Citation

> US National Institutes of Health, RePORTER application 10165245, Assisted living communities- transforming predictive data into proactive care for COVID-19 (3P30AG031679-10S3). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10165245. Licensed CC0.

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