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DIHI Announces 2019 RFA Innovation Awards

The Duke Institute for Health Innovation (DIHI) has announced that it will support high-potential innovation projects in the areas of population health and analytics, novel patient interactions, new and team-based models of care, and optimization of patient flow.

This is the sixth year the institute has funded projects through a call for innovation applications (RFA) across Duke Health.

In a continued commitment to drive innovation in health and healthcare, DIHI will support 10 projects in 2019. We received 75 high-quality applications from across the university and clinical enterprise. In addition to providing funding to advance these selected projects, DIHI will work with various Duke Health and Duke University partners to provide access to data, analytics, statistical analysis, machine learning and AI resources, while driving project and implementation management.

“In an environment of value-based care, having an effective platform for innovation will be one way to ensure that we continue to focus on new models of care,” said William J. Fulkerson Jr., MD, executive vice president of Duke University Health System and executive director of DIHI. “Delivering highest-quality care while exceeding quality goals and enhancing operational efficiencies will remain priorities for our clinical enterprise in the foreseeable future.”

2019 DIHI Innovation Projects and Principal Investigators are:


High-Sensitivity Cardiac Troponin (hsTn) Decision Aid

James Tcheng, MD, Jedrek Wosik, MD, and Duke hsTn Workgroup


Detection and Treatment of Patient Deterioration

Cara O’Brien, MD, Armando Bedoya, MD, MMCi, Benjamin Goldstein, PhD, Cory Miller, RN, Ricardo Henao, PhD, and Mark Sendak, MD, MPP


Understanding Underlying Health Care Costs to Deliver Value-Based Care

Joshua Watson, MD and David Thompson, MD


Use of Telehealth Video Conferencing to Improve the Hospital to SNF Care Transition

Aubrey Jolly Graham, MD, Heidi White, MD, Jonathan Fischer, MD, and Colette Allen, NP


Machine Learning Models to Recognize Early Clinical Deterioration in DUHS Pediatric Inpatients

Jennifer Li, MD, Kimberly Jackson, MD, Ira Cheifetz, MD, Christoph Hornik, MD, Kevin Hill, MD, Jeffrey Langdon, MHA, Sarah Tallent, PNP, Sharah Collier, RN, Remi Hueckel, DNP, Kristin Corey, Lauren Chisholm, MF, RRT-NPS, and Sallie Permar, MD, PhD


Using Machine Learning in Emergency Department Patient Flow

Jason B. Theiling, MD, Neel Kapadia, MD, Cara O’Brien, MD, Michael Gao, and Mark Sendak, MD, MPP


Transforming Cancer Care: Bringing PCPs Back into Cancer Care through Onco-Primary Care

Kevin Oeffinger, MD and Leah Zullig, PhD


Automated Support to Streamline Patient-Provider Messaging (Chatbot)

Jedrek Wosik, MD, Julie O’Brien, PhD, Ricardo Henao, PhD, Manesh Patel, MD, Larry Carin, PhD, and Eric Poon, MD


Machine-learning Algorithm to Predict ICU Readmissions in the Cardiothoracic Surgical Population

Mihai Podgoreanu, MD, Jacob N. Schroder, MD, Jill Engel, DNP, Kelly Kester, MSN, Mary Lindsay, MSN, Nathan Waldron, MD, Quintin Quinones, MD, PhD, Ricardo Henao, PhD, Kristin Corey, Michael Gao, Ray Shao, MD, Ashok Bhata, Tracey Hughes MMCi, and Noa Segall, PhD


Telehealth Examination versus Clinical Examination in the Detection of Rotator Cuff Tears.

Jocelyn Wittstein, MD, Tally Lassiter, MD, MHA, Emily Vinson, MD, Chad Cook, PT, PhD, MBA, Alex Cho, MD, MBA, Donny Phinney, RN, Chad Mather, MD, MBA, Shilpa Shelton, MHA, and Emily Reinke, PhD


[Originally posted by Duke Institute for Health Innovation — March 19, 2019]


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