# Machine learning validation of medication regimen complexity for critical care pharmacist resource prediction

> **NIH AHRQ R21** · UNIVERSITY OF GEORGIA · 2022 · $151,478

## Abstract

Intensive care unit (ICU) patients are at heightened risk of adverse drug events (ADEs) and poor outcomes.
Critical care pharmacists (CCPs) prevent ADEs, improve patient outcomes, and reduce healthcare costs
through performing medication interventions. However, CCPs are an underused healthcare resource due to
lack of health information technology (IT)-based predictive tools to allocate the care they provide to ICU
patients. Currently, there are no validated health IT tools for CCPs available to optimize patient-centered care.
The central hypothesis of this R21 Health Information Technology to Improve Health Care Quality and
Outcomes Award, based on preliminary data, is that data-driven methods applied to the MRC-ICU Scoring
Tool will out-perform predictions of a rules-based model in predicting CCP interventions that can improve
patient outcomes and may serve as the foundation for development of novel health IT tools that optimize the
patient-centered care provided by CCPs. The MRC-ICU Scoring Tool is the first tool designed to measure
medication regimen complexity in ICU patients. To be scaled-up, this tool requires thorough validation and IT
based automation. The objective of this work is to apply machine learning (ML) methodology to multi-center
data to create prediction tools for integration into visualization dashboards that answer vital questions including
(1) what are the predicted number of CCP interventions per patient; (2) what is the risk of real-time modifiable
outcomes (e.g., fluid overload); (3) what are the predicted outcomes (e.g., mortality, length of stay). 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 resource deployment. The rationale for
this work is that it will establish the MRC-ICU Scoring Tool as a means of synthesizing patient data for
integration across EHR systems. The central hypothesis will be tested using large, multi-center data of ICU
patients via these specific aims: (1) Apply ML-based prediction methods to develop a new model of medication
regimen complexity as a metric for predicting CCP interventions and patient outcomes; (2) Compare the
performance of different models to predict CCP interventions and patient outcomes; (3) Design a web-based
dashboard (ICView) to visualize medication regimen complexity-based predictions. The health IT product will
result in a Web-based dashboard (ICView) that houses a real-time, automated MRC-ICU Scoring Tool in
addition to prediction models for CCP interventions that can improve patient outcomes. This innovative
approach applies state-of-the-art ML methodology to the novel MRC-ICU Scoring Tool. The proposed work is
significant because any advances in the understanding of how CCPs improve outcomes would have a
profound public health impact due to their established role on the interprofessional healthcare team. The health
IT products provide the nec...

## Key facts

- **NIH application ID:** 10448856
- **Project number:** 1R21HS028485-01A1
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Andrea Sikora
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $151,478
- **Award type:** 1
- **Project period:** 2022-04-08 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448856

## Citation

> US National Institutes of Health, RePORTER application 10448856, Machine learning validation of medication regimen complexity for critical care pharmacist resource prediction (1R21HS028485-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10448856. Licensed CC0.

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