# Predictive Monitoring: IMPact of Real-time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT)

> **NIH AHRQ R01** · UNIVERSITY OF VIRGINIA · 2024 · $400,000

## Abstract

Keim-Malpass/Bourque
PM-IMPACCT
PROJECT SUMMARY/ABSTRACT
Patients on the acute care wards who deteriorate and are emergently transferred to the intensive care unit
have poor outcomes. Early identification of subtly worsening patients might allow for earlier clinical action
leading to reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring
(i.e. artificial intelligence (AI)-based risk prediction) make a wealth of data available to healthcare providers and
can form the foundation for computational algorithms that integrate real-time bedside monitor physiologic data
to provide early warning of potentially catastrophic physiologic events. The future of acute hospital care
includes monitoring systems that integrate data streams of rapidly changing clinical information to estimate and
communicate risk of imminent events. This will allow a paradigm change in care from reactive to proactive by
predicting patient trajectories and acting early to promote optimal patient trajectories. Predictive analytics
monitoring is a promising technology that will yield families of these new tools. Here, we propose a multi-
disciplinary cluster randomized controlled trial (NCT04359641) to test the use of CoMET (Continuous
Monitoring of Event Trajectories), an AI-based visual analytic that displays risk estimates for multiple adverse
outcomes. It is expected that having access to a visual risk analytic for impending catastrophic outcomes can
draw the clinician’s attention to patients warranting early or extra consideration. Specifically, in our proposed
cluster RCT we will evaluate the impact of predictive analytics monitoring on: (1) improvement in patient
outcomes, (2) response time to proactive clinical action, and (3) costs to the healthcare system. This proposal
is led by an immensely promising interdisciplinary mPI early stage investigators who are members of the
Center for Advanced Medical Analytics. This proposal will strengthen the ability of health care organizations to
evolve as learning health systems that apply bioinformatics data to improve patient outcomes by incorporating
artificial intelligence into knowledge tools that are successfully integrated for use by health care providers and
determine if they improve patient outcomes. We anticipate developing standard processes that can be
leveraged and are scalable for general implementation of predictive analytics monitoring algorithms in real-life
practice contexts. Additionally, we anticipate building on this R01 with future work including a multi-center
randomized control trial testing effectiveness of our artificial intelligence-based risk analytic.

## Key facts

- **NIH application ID:** 10904705
- **Project number:** 5R01HS028803-03
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Jamieson MacDonald Bourque
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $400,000
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10904705

## Citation

> US National Institutes of Health, RePORTER application 10904705, Predictive Monitoring: IMPact of Real-time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT) (5R01HS028803-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10904705. Licensed CC0.

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