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    Andrew Taylor, MD, MHS

    Associate Professor of Emergency Medicine and of Bioinformatics & Data Science
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    Additional Titles

    Director of Artificial Intelligence and Data Science, Emergency Medicine

    About

    Titles

    Associate Professor of Emergency Medicine and of Bioinformatics & Data Science

    Director of Artificial Intelligence and Data Science, Emergency Medicine

    Biography

    Andrew Taylor MD, MHS is an Associate Professor of Biomedical Informatics and Data Science, Emergency Medicine, and Biostatistics at Yale, where he founded and leads the Yale Interdisciplinary AI & Medicine Lab (Y-IAML).

    Y-IAML is a pioneering collaborative research group dedicated to advancing the field of AI in Medicine through a unique cross-disciplinary approach focused on harmoniously blending AI with healthcare delivery. Y-IAML brings together experts in design, cognitive science, behavioral economics, artificial intelligence, implementation science, ethics/philosophy, and decision theory to develop innovative AI solutions that are not only technically robust but also ethically informed and practically implementable. By bridging the gap between diverse fields of study, Dr. Taylor and his team aim to create AI technologies that are deeply attuned to the complexities of healthcare, focusing on patient-centered outcomes and transformative healthcare solutions. Dr. Taylor's goal is to lead the way in interdisciplinary AI research, fostering a new era of healthcare innovation that is inclusive, effective, and profoundly impactful.

    Dr. Taylor's work is generously supported by a diverse group of funding agencies including multiple NIH Institutes (NIDA, NIA, NIMDH, NLM), AHRQ, SIDM, the Gordon and Betty Moore Foundation as well as industry partnerships.

    Dr. Taylor earned his undergraduate degree in physics from the University of Mississippi. He completed medical school at Emory University School of Medicine and Emergency Medicine residency at the University of Connecticut. Most recently he completed fellowships in point-of-care ultrasound and Masters in Health Science with an informatics focus from Yale University. He lives in Durham, CT with his wife and four kids.

    Appointments

    • Biomedical Informatics & Data Science

      Associate Professor on Term
      Fully Joint

    Other Departments & Organizations

    Education & Training

    MHS
    Yale University School of Medicine (2015)
    Informatics Fellowship
    Yale University School of Medicine (2015)
    Ultrasound Fellowship
    Yale University School of Medicine (2011)
    MD
    Emory University (2007)

    Research

    Overview

    Richard Andrew Taylor M.D. is Assistant Professor of Emergency Medicine and Director of Clinical Informatics and Analytics. His work focuses on applying data science to various aspects of emergency care. Prior work has included developing high performance prediction algorithms for urinary tract infections, sepsis severity, and hospital admissions; cost-effective analyses for diagnostic imaging, and research in point-of-care ultrasound outcomes. He is currently the PI on several grants supporting the development of better learning systems in healthcare and is a co-investigator on a PCORTF grant creating better data infrastructure for opioid used disorder. He has methodologic expertise in machine learning, databases, and the secondary use of electronic health record (EHR) data for research.

    Current areas of research:

    Machine learning/Deep learning for predictive analytics– Emergency medicine is a unique and exciting field for the application of predictive analytics. Providers must make numerous decisions (admission/discharge; ordering tests, medications, etc.) in a chaotic environment within a compressed time-frame that can lead to a variety of cognitive errors. Our lab is focused on augmenting this decision process and lessening the cognitive burden of providers through integration of machine learning tools into clinical work-flows. To accomplish this task, we use a variety of methods including deep learning.

    Data Mining/Unsupervised Learning– Adoption of EHRs has led to an explosion of secondary data available for research. We use of variety of data science tools to mine EHR emergency medicine data, find novel relationships, and gain better insight into care processes. Our current research is focused on finding low-dimensional representations of ED encounters and using cluster analysis for phenotype discovery.

    Discovery of optimal pathways of care through the use of decision analysis– Our work in this area is primarily focused on establishing appropriate testing thresholds and cost-effective clinical pathways for emergency conditions including: aortic dissection, renal colic, trauma, and head injury.

    EHR-driven, outcomes-based research– Current work in this area focuses on causal analysis of difficult to randomize interventions in emergency research using observational EHR data. For example, we are interested in examining the effect of point-of-care ultrasound on mortality and other patient-centered outcomes.

    Medical Research Interests

    Artificial Intelligence; Data Mining; Data Science; Decision Theory; Deep Learning; Medical Informatics; Natural Language Processing; Neural Networks, Computer

    Public Health Interests

    Health Care Quality, Efficiency; Health Informatics

    Research at a Glance

    Yale Co-Authors

    Frequent collaborators of Andrew Taylor's published research.

    Publications

    2024

    See All Publications

    Academic Achievements & Community Involvement

    • honor

      University of St Andrews Global Fellow

      International AwardUniversity of St Andrews

    Clinical Care

    Overview

    Richard Andrew Taylor, MD, MHS, is an emergency medicine physician who says he was drawn to his specialty because of its variety.

    “I knew I liked too many things and what I love about emergency medicine is that it’s always new,” Dr. Taylor says. “You get to see every aspect of human life. You see people at their happiest and saddest moments and all the emotional states in between, plus every possible condition in medicine.”

    Realizing that a trip to the emergency department can be scary for patients, Dr. Taylor says he remains calm and takes the time to give patients a picture of what’s going on. “I explain what their medical condition might mean, what possible outcomes are and answer any questions they have,” he says. “I want them to understand what the next steps are and what they need to know if they are being admitted or discharged.”

    Dr. Taylor’s research interests focus on applying artificial intelligence and data science to solve problems in emergency medicine. This includes using machine learning (a branch of artificial intelligence) to predict future trends and outcomes, natural language processing to analyze text, and computer vision to process biomedical imaging.

    The emergency room, Dr. Taylor says, is a natural fit for the the continuing advances being made in data sciences. “It is a fast-paced, high-volume setting where decisions have to made quickly,” he says. “Some of these technologies have the potential to improve care and reduce the cognitive burden.”

    Clinical Specialties

    Emergency Medicine

    Board Certifications

    • Emergency Medicine

      Certification Organization
      AB of Emergency Medicine
      Latest Certification Date
      2022
      Original Certification Date
      2011

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