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This month features James McClay, MD, MS, FACEP, FAMIA, Chief Research Informatics Officer, School of Medicine, University of Missouri, Columbia Missouri.
Balancing Efficacy and Computational Burden: Weighted Mean, Multiple Imputation, and Inverse Probability Weighting Methods For Item Non-response in Reliable Scales Read the article Moderator Presenter Statement of Purpose In survey research, handling missing data is essential for producing valid inferences. Multiple imputation (MI) is widely regarded as the gold standard [...]
This episode discusses real-world practice and research in environmental and public health informatics with the chair-elect of the Climate, Health, and Informatics Working Group, leader of the Public Health Informatics Working Group, and 2024 AMIA Leadership Award Recipient.
The explosion of biomedical big data and information over the past decade has created new opportunities for discoveries that can improve the treatment and prevention of human diseases. As a result, the field of medicine is undergoing a paradigm shift driven by AI-powered analytical solutions. This talk will present the [...]
Listen in to this episode with PhD Candidate Jaysón Davidson on his educational path to informatics and his work with data and social determinants of health.
As health systems grow, clinical informatics leaders play a critical role in selecting and deploying AI solutions that enhance documentation, coding accuracy, and clinician workflows. Ambient AI technology holds immense potential—but how do you evaluate these tools for specialty-specific needs, adoption at scale, and real ROI, and how do you [...]
In this webinar, a panel of Internal Medicine and Pediatrics primary care informaticists will examine equity considerations around emerging generative AI solutions such as ambient listening documentation, patient portal draft replies, and generative summaries for patient education. Drawing on their experience at the intersection of health equity and technology, the [...]
Translational research in Artificial Intelligence (AI) for healthcare has long been constrained by the lack of robust, diverse, and accessible data resources. The newly launched CRITICAL dataset addresses this challenge by providing an unprecedented resource to accelerate innovation in critical care and beyond.