Right intervention reaches the right patient at the right time
A bold new initiative at the University of Iowa to advance how healthcare is delivered—using data, AI, and clinical expertise to improve patient outcomes where it matters most.
The Clinical Analytics Research Collaborative (CARC) brings together leading experts from business, engineering, medicine, and nursing—alongside University of Iowa Health Care (UIHC)—to accelerate the use of artificial intelligence, advanced analytics, and machine learning in real-world clinical challenges and decision-making.
By aligning talent, technology, and partnerships, the Collaborative will ensure the right intervention reaches the right patient at the right time.
Our purpose
The CARC is a multidisciplinary research initiative designed to:
- Advance AI-driven clinical decision support in areas such as risk prediction, treatment optimization, and symptom management
- Integrate computing and clinical sciences to address day-to-day healthcare challenges that remain underexplored
- Coordinate and scale existing collaborations to pursue large federal grants (NIH, AHRQ, PCORI) and philanthropic investment
- Facilitate secure, privacy-preserving collaboration through federated learning models that enable cross-institutional research without sharing patient-level data
- Support workforce development, including postdoctoral scholars and PhD students working at the intersection of analytics and healthcare
"Our goal is transformational: helping every patient benefit from the best insights modern medicine can provide."
Why it matters
Healthcare systems generate unprecedented amounts of data—but too often, that data is underutilized at the point of care. The Collaborative bridges this gap, translating data into actionable insights that improve outcomes for patients across Iowa and beyond.
With a focus on rural, aging, and veteran populations, the CARC addresses some of the most pressing healthcare needs of our time.
# 2
Iowa Cancer Registry (2026)
57 %
March of Dimes (2024)
33 %
Iowa Health and Human Services (2026)
Impact in action
By embedding advanced analytics into clinical decision-making, patient care will become more precise, timely, and effective. Current and planned initiatives include:
Clinical AI applications
- Antimicrobial resistance prediction to support “smart antibiograms” that optimize treatment and reduce widespread resistance
- Medication titration models for chronic disease management, including heart disease, hypertension, and post-transplant immunosuppression
- Pregnancy complication risk prediction, including preeclampsia and stillbirth, to guide earlier intervention and monitoring
Advanced treatment & outcomes
- Transplant graft failure prediction across kidney, liver, and corneal transplantation
- Longitudinal chemotherapy symptom prediction to improve symptom management in cancer care
- Cancer treatment planning using agentic AI and reinforcement learning
Personalized risk identification
- Mental health risk assessment using advanced language models
- Hospital-acquired pressure injury (HAPI) risk prediction
Building the future of clinical decision-making
By aligning disciplines, scaling collaboration, and focusing on measurable health impact, the Collaborative will transform how data informs care—for Iowa patients and communities nationwide. It will establish the state’s hub for integrating state-of-the-art computing, large-scale health data, and world-class clinical expertise.
The result: improved patient outcomes, more efficient healthcare delivery, and a national model for interdisciplinary collaboration.
Getting started: recruit a graduate research assistant and postdoctoral scholar to scale up cancer-specific applications and build a dedicated fund to support priority projects and emerging opportunities.
Clinical Analytics Research Collaborative Leadership
Nick Street
Associate Dean for Research & PhD Programs, Professor and Hadley Chair, Tippie College of Business
A nationally recognized leader in healthcare machine learning, Dr. Street has helped shape how data informs diagnosis, risk assessment, and treatment—leading collaborative efforts backed by NSF and NIH support.
Patrick Fan
Henry B. Tippie Excellence Chair and Professor of Business Analytics, Tippie College of Business
A top 1% most-cited scholar worldwide, Dr. Fan is a leader in AI and natural language processing with extensive healthcare applications and federal funding support.
Jim Blum
Chief Health Information Officer, UIHC
Associate Professor of Anesthesiology, Carver College of Medicine
Dr. Blum has led UIHC’s advancement in practical AI implementation and brings deep experience integrating analytics into clinical operations.
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