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Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2024. The workshop will be offered in December as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR-based studies.

The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using edata from electronic health records (EHRs). EHR data are a widely available form of real-world data that are being used in different types of studies, spanning clinical trials, comparative effectiveness, risk prediction, population health, and more. The EHR-SDW will introduce learners to the components of EHR data and in considerations for designing effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding), as well as feedback on effective study designs.

This course will be conducted virtually via Zoom.

To register for the EHR-SDW, please visit  https://events.duke.edu/ehr-sdw-2024

To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_a3Fs9TyyK82Bkr4

The deadline for registration is Thursday, November 21, 2024.

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The Duke Spark Initiative for AI in Medical Imaging formally launches this week. Spark’s mission at Duke encompasses research into development and use of artificial intelligence in medical imaging, with an emphasis on collaborative work between physicians across different specialties and machine learning experts.

“Spark represents the kind of collaboration that can realize the full potential of the diverse pool of knowledge, talent, and insight across the School of Medicine and the larger university community,” says Mary Klotman, MD, executive vice president for health affairs at Duke University and the dean of the Duke University School of Medicine. “As AI technologies are increasingly integrated into patient care and research, efforts like Duke Spark will be essential for ensuring that these tools yield meaningful benefits for patients and clinicians.”

An interdepartmental effort convened under the umbrella of Duke AI Health, Spark includes a team of faculty researchers and exceptional trainees drawn from the Departments of Radiology, Surgery, Orthopaedic Surgery, Medicine, Electrical and Computer Engineering, Computer Science, and Biomedical Engineering.

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Duke AI Health Director of Data Science Benjamin Goldstein, PhD, and Interim Director for Duke Center for Autism and Brain Development Lauren Franz, MBChB, have recently been awarded a grant from the National Institutes of Health to develop approaches for using existing sources of health data to better understand late language emergence in children, or “late talking.” Late talking often co-occurs with autism and is frequently “undercoded” in medical records, which can affect whether children and families receive referrals for early intervention. The R21 grant, which is part of the NIH’s cross-institute Tackling Acquisition of Language in Kids (TALK) Initiative, will support efforts at Duke to apply machine learning to compare coding data in electronic health records (EHRs) with free-text information in EHR notes to determine whether indications of late talking recorded there are matched by appropriate coding.

The work will also leverage longitudinal health data and NC-statewide sources of claims data to shed additional light on developmental pathways, including methods for analyzing co-occurring factors that may predispose children to late talking, as well as downstream effects of delayed language acquisition later in life. Another focus for the project will be the health equity dimensions of late talking, including differences across gender, language, and race and ethnicity.

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Duke AI Health has recently welcomed Michael Zavlanos, PhD, as Director in Healthcare System Optimization for Duke AI Health. In this newly created role, Dr. Zavlanos will direct efforts focused on developing and implementing machine learning, optimization, and other algorithmic tools to increase operational efficiency and resource use in health care at Duke.

Currently a professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering and an Amazon Scholar with Amazon Robotics, Dr. Zavlanos received his doctorate in electrical and systems engineering from the University of Pennsylvania, where he specialized in robotics and autonomous control systems. More recently, at Duke, Dr. Zavlanos has begun to explore the potential for using data gathered from “real-world” clinical settings to solve problems with a direct impact for patients.

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Duke Clinical Research Institute and AI Health are delighted to announce the return of Dr. Ricardo Henao to Duke, starting July 1! After a leave of absence at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, Dr. Henao has returned to North Carolina as Associate Director of Clinical Trials AI for the Duke Clinical Research Institute (DCRI) and as a member of AI Health’s Faculty Council. Dr. Henao is Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine.

“We are very excited about Dr. Henao’s return to Duke given his enormous contributions both to teaching and to developing novel methods for applying probabilistic modeling to deep learning algorithms,” notes Duke AI Health Director Michael Pencina, PhD. “Ricardo’s innovative work has been key to multidisciplinary efforts at Duke and beyond to improve predictive modeling for clinical outcomes,” he continues, noting that Henao’s gifts as a collaborator, methodologist, and teacher will be in high demand.

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