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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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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Current medical standards for accessing stroke risk perform worse for Black Americans than they do for white Americans, potentially creating a self-perpetuating driver of health inequities. A study, led by Duke Health researchers and appearing online Jan. 24 in the Journal of the American Medical Association, evaluated various existing algorithms and two methods of artificial intelligence assessment that are aimed at predicting a person’s risk of stroke within the next 10 years. The study found that all algorithms were worse at stratifying the risk for people who are Black than people who are white, regardless of the person’s gender. The implications are at the individual and population levels: people at high risk of stroke might not receive treatment, and those at low or no risk are unnecessarily treated.

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Save the date: February 14, 2023, 12:00 PM EST: Duke AI Health’s Nicoleta Economou, PhD, joins Duke DHTS’s Armando D. Bedoya MD MMCi, to present: ‘Algorithm-Based Clinical Decision Support (ABCDS) Oversight: A framework for the governance and evaluation of algorithms to be deployed at Duke Health.’ During the webinar, which is open to members internal and external to Duke, Drs. Economou and Bedoya will discuss highlights from their recent paper published in the Journal of the American Medical Informatics Association (JAMIA).

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Recent years have seen growing interest in the use of artificial intelligence tools for healthcare applications, including diagnosis, risk prediction, clinical decision support, and resource management. Capable of finding hidden patterns within the enormous amounts of data that reside in patient electronic health records (EHRs) and administrative databases, these algorithmic tools are diffusing across the world of patient care. Often, health AI applications are accompanied by assurances of their potential for making medical practice better, safer, and fairer. The reality, however, has turned out to be more complex.

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