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

Award Number: R21HS028485
ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY
OPDIV: AHRQ
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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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 necessary foundation for a future R18 application for a multi-center, prospective trial to evaluate MRC-ICU based CCP resource allocation strategies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = $142,599 )
2023	2023	UNIVERSITY OF GEORGIA RESEARCH FOUNDATION, INC.	310 EAST CAMPUS RD TUCKER HALL ROOM 409	ATHENS	GA	30602	CLARKE	USA	Research on Healthcare Costs, Quality and Outcomes	000	2	3/23/2023	NON-COMPETING CONTINUATION	$142,599
														Subtotal = $142,599

Issue Date FY: 2022 ( Subtotal = $151,478 )
2022	2022	UNIVERSITY OF GEORGIA RESEARCH FOUNDATION, INC.	310 EAST CAMPUS RD TUCKER HALL ROOM 409	ATHENS	GA	30602	CLARKE	USA	Research on Healthcare Costs, Quality and Outcomes	000	1	4/8/2022	NEW	$151,478
														Subtotal = $151,478

Grand Total All Awards = $294,077

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

Award Number: R21HS028485

ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY

OPDIV: AHRQ

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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