Rabail Baig

Quality Management System (QMS) framework can bridge the AI translation gap, say Duke Researchers

A groundbreaking paper co-authored by Duke Health’s Nicoleta Economou, PhD, and Michael Pencina, PhD, recently published in NPJ Digital Medicine discusses leveraging a Quality Management System (QMS) for AI/ML development intended for healthcare. The authors explain how a tailored QMS framework can bridge the AI translation gap, ensuring safe, ethical, and effective incorporation into patient care. This approach can accelerate the translation of AI research into practical clinical applications, prioritizing patient safety and fostering trust in healthcare innovation.

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Vanderbilt and Duke Awarded Moore Foundation Grant to Improve Oversight of AI Technology in Health Care Systems

Vanderbilt University Medical Center (VUMC) and Duke University School of Medicine were awarded a $1.25 million grant from the Gordon and Betty Moore Foundation for the project “Measuring Artificial Intelligence (AI) Maturity in Healthcare Organizations.”  Working with the Coalition for Health AI (CHAI) and the University of Iowa, a team of experts will leverage the grant to develop a maturity model framework. The project leads are Peter Embí, MD, MS, and Laurie Novak, PhD, MHSA, from VUMC; and Michael Pencina, PhD, and Nicoleta Economou, PhD, from Duke. This framework will outline the essential capabilities that health systems must establish to ensure they are well-prepared for the trustworthy utilization of AI models.

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Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023

Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023. The workshop will be offered December 4th through 8th 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 electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of 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 that they will work on. The course will be conducted virtually via Zoom. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of the Electronic Health Records Study Design Workshop held in December 2022 and highly successful Machine Learning Schools, with 12 events held since 2017. The Duke Machine Learning Schools have reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Our 2022 Duke Machine Learning Summer School attracted 140 participants from around the world, representing 41 universities, institutes, and corporations.

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Duke AI Health’s Nicoleta Economou talks guidelines & guardrails for responsible health AI development in AIMed “Champions” interview

Nicoleta Economou-Zavlanos, PhD, the director of Governance and Evaluation of health AI systems at Duke AI Health, was recently interviewed by AIMed’s Gemma Lovegrove for their AI Champions Interview Series, which highlights key thought leaders in the AI space. During the interview, Dr. Economou underscored the importance of incorporating fairness, transparency, and inclusivity throughout the entire process of health AI development, implementation, and monitoring.

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CHI Conference on Artificial Intelligence and the Future of Digital Healthcare

The Connected Health Initiative (CHI) is hosting an in-person conference titled ‘Artificial Intelligence and the Future of Digital Healthcare at the Crossroads’ on September 26, 2023, at the National Press Club in Washington, D.C., from 12:30 PM to 5:35 PM EDT. The event will delve into the profound impact of AI systems on healthcare, offering potential for improved outcomes, cost savings, and a shift towards proactive disease prevention. Duke AI Health Director Michael Pencina, PhD, ABCDS Director Nicoleta Economou-Zavlanos, PhD, and AI Health Equity Scholar Michael Cary, PhD, RN, will be presenting the Algorithm-Based Clinical Decision Support (ABCDS) Oversight framework at the conference, touching upon the program’s design, implementation and strategies for bias mitigation and ensuring health equity. The CHI conference aims to foster a vital public dialogue on the state of health AI, proactive approaches by leading organizations to address AI efficacy, and the government’s role in managing AI’s risks and opportunities in healthcare.

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Duke AI Health Director Michael Pencina Named Duke Health’s First Chief Data Scientist

Michael Pencina, PhD, vice dean for data science, professor of biostatistics and bioinformatics at Duke University School of Medicine, and director of Duke AI Health, has been named Duke Health’s first chief data scientist. Executive Vice President for Health Affairs and Dean Mary E. Klotman, MD, and Duke University Health System Chief Executive Officer Craig Albanese, MD, MBA, announced Pencina’s appointment. “In the current era of rapid expansion of AI and data science, we created this new role in recognition of the need for a well-articulated strategy for Duke Health that spans and connects both our academic and our clinical missions,” Klotman and Albanese said in their announcement.

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Duke Health and Microsoft Form AI Partnership to Advance Medicine

Duke University and Duke Health have recently announced a monumental five-year strategic partnership with Microsoft to support artificial intelligence (AI) applications in medicine and lead transformation in healthcare delivery, champion health equity, and pioneer advanced research. “We are excited to partner with Microsoft and bring our organizations’ talent together to solve the most pressing healthcare challenges,” said Duke AI Health Director and Vice Dean for Data Science Michael Pencina, PhD. “We will combine medical expertise, data science methods, and technology solutions to improve patient care and community health and advance the foundations of trustworthy health AI.”

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Duke Researchers Develop Prediction Model to Identify Children With Complex Health Needs At Risk for Hospitalization

An important study led by Duke’s David Ming, MD, and AI Health’s Benjamin Goldstein, PhD, and Nicoleta Economou, PhD, on the use of predictive modeling to identify children with complex health needs who are at high risk for hospitalization, was recently published in Hospital Pediatrics, the official journal of the American, Academy of Pediatrics. The study analyzed data from electronic health records and found that certain demographic, clinical, and health service use factors were associated with a higher risk of future hospitalization. The authors, including Duke’s Richard Chung, MD, and Ursula Rogers, BS, suggest that the use of predictive modeling can help identify children with complex health needs who may benefit from targeted interventions to prevent hospitalizations and improve outcomes. The study is accompanied by a commentary by University of Wisconsin Neil Munjal, MD, MS, titled ‘Machine Learning: Predicting Future Clinical Deterioration in Hospitalized Pediatric Patients,’ which describes the Duke researchers’ machine learning approach as “thought-provoking.”

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Coalition for Health AI Unveils Blueprint for Trustworthy AI in Healthcare

The Coalition for Health AI (CHAI) released its highly anticipated “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare” (Blueprint). The Blueprint addresses the quickly evolving landscape of health AI tools by outlining specific recommendations to increase trustworthiness within the healthcare community, ensure high-quality care, and meet healthcare needs. The 24-page guide reflects a unified effort among subject matter experts from leading academic medical centers and the healthcare, technology, and other industry sectors, who collaborated under the observation of several federal agencies over the past year.

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AI Health Seminar Series Success from 2022

The fall 2022 semester was comprised of 9 AI Health seminars that attracted 463 attendances, including people who attended multiple sessions. Across all of 2022, the AI Health seminar series has hosted 22 virtual seminars with 1,820 cumulative attendances. The AI Health seminar series builds upon the success of our previous “Plus Data Science” learning experiences from 2017 – 2021 under the leadership of Larry Carin, which convened 59 in-person learning experiences and 66 virtual learning experiences. Since its launch in 2018, +DS has held a cumulative 125 learning experience sessions (both in-person and virtual). – Metrics by Tiffany Torres

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