1,000+ Opportunities
Find the right grant
Search federal, foundation, and corporate grants with AI — or browse by agency, topic, and state.
This listing may be outdated. Verify details at the official source before applying.
Find similar grantsArtificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction is sponsored by Agency for Healthcare Research and Quality (AHRQ). Develops AI tools to guide critical care pharmacists in preventing medication errors and adverse drug events.
Get a weekly digest of new grants like this
A free weekly digest of new foundation and federal funding opportunities as they're added to Granted. Unsubscribe anytime.
Or search similar grants →Extracted from the official opportunity page/RFP to help you evaluate fit faster.
Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction | Digital Healthcare Research Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Developing artificial intelligence-based health information technology tools to guide critical care pharmacists in preventing medication errors has the potential to optimize resources and improve patient safety by reducing adverse drug events.
AHRQ Health Services Research Projects (R01) Principal Investigator(s) Medication regimens for intensive care unit (ICU) patients are complex, leading to these patients having a three times greater risk of adverse drug event (ADEs), with over 1. 5 million individuals in ICUs each year experiencing potentially life-threatening ADEs.
Although most ADEs are preventable, a patient experiencing a single ADE doubles their chance of mortality and is a significant financial burden on the healthcare system.
Working within ICUs, critical care pharmacists (CCPs) make recommendations to the ICU team around medication interventions, such as laboratory monitoring, dose adjustment, identification of drug-drug interactions, order entry error correction, and alternative medications with lower risk. As such, CCPs can prevent ADEs, improve patient-centered outcomes, and reduce healthcare costs.
However, CCPs provide care for a high number of ICU patients at one time, resulting in high cognitive loads and issues with effectively allocating their time. Strategies to optimize CCP care hold the potential to make patient care safer, more efficient, and with improved resource allocation.
To address this need, artificial intelligence (AI) and machine learning (ML) will be used to create algorithms for predicting which patients are at risk by defining intervenable events based on patient features, opportunities for intervention, and those associated with poor outcomes.
The researchers will leverage a previously-developed tool called the medication regimen complexity-intensive care unit (MRC-ICU) Scoring Tool, which will be integrated into visualization dashboards for CCPs, called ICView. The specific aims of the research are as follows: Create robust prediction models of intervenable events to guide CCP medication interventions.
Explore causal relationships among intervenable events, CCP interventions, and outcomes. Design an electronic health record-integrated platform (ICView) to visualize predictions to guide CCP care. The researchers will apply AI and ML methodology using data from multiple centers to create these integrated prediction tools.
The researchers will focus on understanding the best metric for predicting ICU intervenable ADEs and CCP workload, the causal factors of intervenable events that can be prevented by CCPs, and how CCPs are able to efficiently use AI-based predictions at the bedside.
Applying user-centered design methods, researchers will create the visualization dashboard called ICView that houses MRC-ICU-based AI-informed prediction models for CCP interventions. It is anticipated that by identifying intervenable events, reducing the cognitive workload of CCPs, and better allocating the time of CCPs, ADEs will be reduced, and patient outcomes will improve.
Machine learning-based prediction of prolonged duration of mechanical ventilation using medication data. Citation: Murray B, Zhao B, Chen Z, Smith SE, Kong Y, Shen Y, Li S, Chen X, Sikora A; MRC‐ICU Investigator Team. Machine learning-based prediction of prolonged duration of mechanical ventilation using medication data.
Pharmacotherapy. 2026 Feb;46(2):e70090. doi: 10.
1002/phar. 70090. Epub 2026 Jan 6.
PMID: 41495589. Link: https://pubmed. ncbi.
nlm. nih. gov/41495589/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Quantitative , Retrospective Technology: Machine Learning , Predictive Modeling Population: Adults , Patient Prediction of pharmacist medication interventions using medication regimen complexity.
Citation: Zhao B, Shen Y, Devlin JW, Murphy DJ, Smith SE, Murray B, Rowe S, Sikora A. Prediction of pharmacist medication interventions using medication regimen complexity. JAMIA Open.
2025 Nov 3;8(6):ooaf138. doi: 10. 1093/jamiaopen/ooaf138.
PMID: 41190317; PMCID: PMC12582545. Link: https://pubmed. ncbi.
nlm. nih.
gov/41190317/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Observational Study , Quantitative , Retrospective Technology: Machine Learning , Predictive Modeling Population: Adults , Patient , Pharmacist Large language models management of complex medication regimens: A case-based evaluation.
Citation: Chase A, Most A, Xu S, Barreto E, Murray B, Henry K, Smith S, Hedrick T, Chen X, Li S, Liu T, Sikora A. Large language models management of complex medication regimens: A case-based evaluation. Front Pharmacol.
2025 Nov 24;16:1514445. doi: 10. 3389/fphar.
2025. 1514445. PMID: 41368579; PMCID: PMC12682882.
Link: https://pubmed. ncbi. nlm.
nih.
gov/41368579/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Case Study , Chart Review , Data Collection , Descriptive , Quantitative Technology: Clinical Decision Support System , Large Language Models , Natural Language Processing System Population: Clinical Staff/Clinician PharmacyGPT: Exploration of artificial intelligence for medication management in the intensive care unit.
Citation: Liu Z, Xu S, Wu Z, Murray B, F Barreto E, Li S, Liu W, Li X, Liu T, Sikora A. PharmacyGPT: Exploration of artificial intelligence for medication management in the intensive care unit. BMC Med Inform Decis Mak.
2025 Oct 28;25(1):398. doi: 10. 1186/s12911-025-03230-1.
PMID: 41152866; PMCID: PMC12570815. Link: https://pubmed. ncbi.
nlm. nih.
gov/41152866/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Chart Review , Quantitative , Retrospective Technology: Clinical Decision Support System , Large Language Models , Predictive Modeling Population: Adults , Patient A letter addressing the impact of a flex ICU position on workload.
Citation: Smith BA, Henry KR, Nguyen K, Sikora A. A letter addressing the impact of a flex ICU position on workload. Hosp Pharm.
2025 Feb 13:00185787251318287. doi: 10. 1177/00185787251318287.
Epub ahead of print. PMID: 39959186; PMCID: PMC11826811. Link: https://pubmed.
ncbi. nlm. nih.
gov/39959186/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Quantitative , Prospective Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education.
Citation: Yang H, Hu M, Most A, Hawkins WA, Murray B, Smith SE, Li S, Sikora A. Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education. Front Artif Intell.
2025 Jan 9;7:1514896. doi: 10. 3389/frai.
2024. 1514896. PMID: 39850846; PMCID: PMC11754395.
Link: https://pubmed. ncbi. nlm.
nih.
gov/39850846/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Analysis , Quantitative Technology: Artificial Intelligence , Deep Learning , Large Language Models , Medication Management System Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.
Citation: Henry K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Most A, Sikora A; MRC‐ICU Investigator Team. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. Pharmacotherapy.
2025 Feb;45(2):76-86. doi: 10. 1002/phar.
4642. Epub 2025 Jan 3. PMID: 39749877; PMCID: PMC11834896.
Link: https://pubmed. ncbi. nlm.
nih. gov/39749877/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Research Method: Quantitative , Retrospective Technology: Machine Learning , Medication Management System Evaluation of critical care pharmacist evening services at an academic medical center.
Citation: Chase AM, Forehand CC, Keats KR, Taylor AN, Jones TW, Sikora A. Evaluation of critical care pharmacist evening services at an academic medical center. Hosp Pharm.
2024 Apr;59(2):228-233. doi: 10. 1177/00185787231207996.
Epub 2023 Nov 1. PMID: 38450349; PMCID: PMC10913874. Link: https://pubmed.
ncbi. nlm. nih.
gov/38450349/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Document Type: Journal Publication Research Method: Prospective , Quantitative Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.
Citation: Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Chen X, Buckley MS, Rowe S, Devlin JW. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Sci Rep.
2023 Nov 10;13(1):19654. doi: 10. 1038/s41598-023-46735-3.
PMID: 37949982; PMCID: PMC10638304. Link: https://pubmed. ncbi.
nlm. nih.
gov/37949982/ Principal Investigator: Sikora, Andrea Project Name: Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Document Type: Journal Publication Research Method: Observational Study , Quantitative , Retrospective Technology: Machine Learning Use of Artificial Intelligence and Machine Learning to Improve Care by Critical Care Pharmacists Supporting Health Systems in Advancing Care Delivery Using Digital Healthcare Tools to Improve Patient Safety Using machine learning- and artificial intelligence-developed tools in the ICU has the potential to optimize critical care pharmacist resources and improve patient safety by reducing adverse drug events.
Critical care pharmacists have the expertise to handle patients that have complex medical needs In intensive care units (ICUs), critical care pharmacists (CCPs) are integral members of healthcare teams. CCPs analyze and manage the highly complex medication regimens of ICU patients and work to identify and provide guidance for medication-related problems.
Research shows that having a pharmacist on rounds in the ICU has significant benefits, including reduction of medication errors and adverse drug events (ADEs), improved patient outcomes, reduced costs, and most importantly, reduced risk of death by 20 percent. But access to these pharmacists in the United States is lacking.
Not all ICUs have CCP care and even when they do, the CCP is often caring for many patients—up to 50 or more at a time. This leads to high cognitive load and difficulty effectively managing time to provide for the patients most in need of CCP care. As a CCP herself, Dr. Andrea Sikora sees firsthand how ICU patients benefit from having a CCP on the care team.
With two recently awarded AHRQ-funded grants, she is studying the challenges that can be created by these gaps in CCP care and availability and how technology can better support their work to improve patient-centered outcomes.
Implementing a tool can quantify the complexity of a patient’s medication regimen and predict potential ADEs In the first study, Dr. Sikora and her team at the University of Georgia are using artificial intelligence and machine learning to create algorithms for predicting which patients are at risk for ADEs based on patient features, opportunities for intervention, and those associated with poor outcomes.
The tool, called the Medication Regimen Complexity Intensive Care Unit, or MRC-ICU score, quantifies the complexity of a patient’s medication regimen to predict ADEs that could be prevented by timely CCP intervention. The tool will be integrated into visualization dashboards, called ICView, that guide CCPs in preventing ADEs in patients, thus improving patient safety.
Using machine learning to predict when pharmacists are needed for critical care In the second research study, the team plans to develop and validate machine learning predictive models to optimize the workflow for CCPs by identifying which patients need CCP intervention. The models will be integrated into the MRC-ICU tool and will summarize CCP workload through predicting total CCP interventions.
The models will also guide CCP care by predicting ADEs that may be prevented by CCP intervention. Identifying which interventions—and under which conditions—can improve outcomes by helping administrators better determine workload, such as optimal CCP-to-ICU ratios through providing workload insights.
“That tool quantifies the complexity of what the patient is taking in the ICU with the goal that it's going to help predict how much effort you need from a pharmacist. If you knew that, then you could say, ‘Okay, this patient needs an hour of effort, and we have 20 patients in the ICU. ’ Well, that's 20 hours, so that is probably more than one 8-hour shift.
And so, you'd be able to have those kinds of conversations around workload. ” Collectively, Dr. Sikora’s innovative AHRQ research shows the exciting ways that machine learning and artificial intelligence can be used in healthcare.
In addition to benefiting patients by facilitating CCPs interventions when medication-related problems are found, the research can benefit the healthcare system at large by providing the justification for the critical role that CCPs play.
This Research is Featured in Improving Healthcare Through AHRQ's Digital Healthcare Research Program: 2022 Year in Review Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction Machine Learning Validation of Medication Regimen Complexity for Critical Care Pharmacist Resource Prediction This Research is Featured in Improving Healthcare Through AHRQ's Digital Healthcare Research Program: 2022 Year in Review A Machine Learning Algorithm to Improve the Use of Interpreters for Hospitalized Patients with Complex Care Needs Safer Inter-Hospital Transfers by Improving Access to Health Information Automated Retract-and-Reorder Measures to Improve Medication Safety Research Themes and Findings Research Themes and Findings DHR 20th Anniversary Blog Series DHR 20th Anniversary Timeline Milestones and Achievements AHRQ Digital Healthcare Research Publications Database Patient-Generated Health Data I Patient-Reported Outcomes Guide to Integrate Patient-Generated Digital Health Data into Electronic Health Records in Ambulatory Care Settings Clinical Decision Support (CDS) CDS Innovation Collaborative (CDSiC) Resource Library Health IT Survey Compendium Time and Motion Studies Database Implementation Toolsets for E-Prescribing Implementation in Independent Pharmacies Implementation in Physician Offices Children's Electronic Health Record (EHR) Format Archived Tools & Resources Digital Healthcare Research Home Digital Healthcare Research Home Digital Healthcare Research Home
According to the current listing, eligibility includes: Public or private entities, including state and local governments; federal agencies are not eligible. Confirm the full requirements in the official notice before applying.
The current listing shows $1,862,521. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Artificial Intelligence-Based Health Information Technology Tools to Optimize Critical Care Pharmacist Resources Through Adverse Drug Event Prediction is funded by Agency for Healthcare Research and Quality (AHRQ). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
As of September 12, NSF had obligated $6.3 billion across 6,200 grants versus $8.1 billion and 8,600 last year. AHRQ has made 61 awards. Judge Allison Burroughs ordered the government to report by September 28 on whether IES will obligate $180 million before it expires. Here is what actually happens to the money on October 1 — and what it means for your FY2027 application.
Read articleUSDA and HHS announced Harvest to Hallways on August 24, 2026: $50M in new cafeteria infrastructure funding on top of $20M in Equipment Assistance Grants, up to $25M more in Patrick Leahy Farm to School awards for FY2026, and $30M in HHS nutrition research. Each pot has a different applicant, a different route, and a different timeline. Here is how to position for all three before the notices post.
Read articleA federal court just blocked the government from terminating grants because 'agency priorities' changed — days after AHRQ used that exact logic to end 78 research awards worth $109M. Here is what 2 CFR 200.340 actually says, what the July 17 ruling does and does not protect, and the defensive playbook every current grantee needs before October 1.
Read article