# Artificial intelligence-based health IT tools to optimize critical care pharmacist resources through adverse drug event prediction

> **NIH AHRQ R01** · UNIVERSITY OF GEORGIA · 2023 · $367,805

## Abstract

Critically ill patients are at heightened risk of adverse drug events (ADEs) that worsen outcomes. Critical care
pharmacists (CCPs) prevent ADEs, improve patient-centered outcomes, and reduce healthcare costs through
performing medication interventions. However, CCPs are an inequitably distributed and non-optimized
healthcare resource due to lack of health information technology (IT)-based predictive tools that can identify
CCP-driven medication interventions that prevent ADEs. To identify these preventative medication
interventions, delineating the medication outcome causal pathway among patient features, medication
interventions, ADEs, and patient-centered outcomes is required. Here, novel causal inference methodologies
incorporating artificial intelligence (AI) and machine learning (ML) will be applied for the first time to medication
safety questions in the ICU. The strategy will focus on developing a novel scoring tool designed for prediction,
the medication regimen complexity-intensive care unit (MRC-ICU) Scoring Tool, to predict intervenable ADEs
in this causal pathway that are predictable by patient features, preventable by CCPs, and otherwise associated
with poor outcomes. The central hypothesis of this AHRQ Health Services Research Project (R01), based on
preliminary data, is that an AI-informed dashboard visualizing the medication outcome causal pathway can
optimize CCP care to improve patient-centered outcomes. The objective of this work is to apply AI and ML
methodology to multi-center data to create prediction tools for integration into visualization dashboards that
answer vital questions including (1) what is the best metric for predicting ICU intervenable events and CCP
workload?; (2) what causal factors of intervenable events can be prevented by CCPs?; (3) how can CCPs
efficiently use AI-based predictions at the bedside? The long-term goal of the proposed work is to establish
validated prediction models that can be embedded into dashboards in the electronic health record (EHR) to
help guide CCP delivered care. The rationale for this work is that it will establish the MRC-ICU Scoring Tool as
a means of predicting medication safety events and CCP interventions. The central hypothesis will be tested
using large, multi-center datasets of ICU patients via these specific aims: (1) Create robust prediction models
of intervenable events to guide CCP medication interventions; (2) Explore causal relationships among
intervenable events, CCP interventions, and outcomes; (3) Design an EHR-integrated platform (ICView) to
visualize predictions to guide CCP care. Applying user centered design methods to create a health IT product
will result in a visualization dashboard (ICView) that houses MRC-ICU based, AI-informed prediction models
for CCP interventions that can improve patient outcomes. This innovative approach applies state-of-the-art ML
methodology to causal outcome predictions using the novel MRC-ICU Scoring Tool. The proposed work is
sig...

## Key facts

- **NIH application ID:** 10692681
- **Project number:** 5R01HS029009-02
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Andrea Sikora
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2023
- **Award amount:** $367,805
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10692681

## Citation

> US National Institutes of Health, RePORTER application 10692681, Artificial intelligence-based health IT tools to optimize critical care pharmacist resources through adverse drug event prediction (5R01HS029009-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10692681. Licensed CC0.

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