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Adjunct Faculty

Adjunct Faculty are scientists or others who are employed outside of the school but have a specific, mutually beneficial role to Yale School of Medicine department programs.

Adjunct rank details

Andrew Taylor, MD, MHS

Associate Professor Adjunct of Biomedical Informatics and Data Science
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About

Titles

Associate Professor Adjunct of Biomedical Informatics and 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.

Last Updated on November 10, 2024.

Appointments

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

2025

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Academic Achievements & Community Involvement

Honors

  • honor

    University of St Andrews Global Fellow

    01/01/2023International AwardUniversity of St Andrews

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Contacts

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