![Andrew Lee Chu, MD](https://profiles.stanford.edu/proxy/api/cap/profiles/297723/resources/profilephoto/350x350.1665088133758.jpg)
Andrew Lee Chu, MD
Clinical Assistant Professor, Emergency Medicine
Bio
Dr. Chu is a board-certified emergency medicine physician. He is a clinical assistant professor in the Department of Emergency Medicine.
He received his applied research fellowship training in healthcare innovation at Harvard Medical School. He completed his residency in emergency medicine at Harvard.
Dr. Chu also earned a Master of Public Health (MPH) degree from Harvard and a Master of Business Administration (MBA) degree from Quantic School of Business and Technology.
He has published in Academic Emergency Medicine Education and Training, the Western Journal of Emergency Medicine, and other peer-reviewed publications. Topics include the use of smartphone applications to help clinicians and trainees manage emergencies at the patient’s bedside.
Dr. Chu has created award-winning apps that provide digital reference tools containing clinical protocols, resources, and other content. These tools focus on acute life-threatening illnesses, advanced cardiac life support, and pediatric advanced life support.
He is a member of the American College of Emergency Physicians, American Academy of Emergency Medicine, and Society of Academic Emergency Medicine.
Clinical Focus
- Emergency Medicine
Honors & Awards
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Horace W. Goldsmith Fellowship, Harvard Business School
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Best Abstract Award, McCahan Medical Campus Education Day, Boston University School of Medicine
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Clinical Quality & Patient Safety Innovation Award, Boston Medical Center Department of Surgery
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Excellence in Medicine Leadership Award, American Medical Association (2017)
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Scholars Award, Massachusetts Medical Society (2018)
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Dr. Richard C. Wuerz Award for Emergency Medicine Research, Harvard Affiliated Emergency Medicine Residency (2021)
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Information Technology Award, Massachusetts Medical Society (2022)
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Bedside Teacher of the Year, Stanford Department of Emergency Medicine (2024)
Professional Education
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Fellowship, Stanford's Byers Center for Biodesign, Biodesign Faculty Fellowship (2024)
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Board Certification: American Board of Emergency Medicine, Emergency Medicine (2023)
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Fellowship, Massachusetts General Hospital Healthcare Transformation Lab, Applied Research in Healthcare Innovation (2022)
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Residency: Massachusetts General Hospital Emergency Medicine Residency (2022) MA
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Medical Education: Boston University School of Medicine (2018) MA
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Masters, Harvard T.H. Chan School of Public Health, Healthcare Management (2018)
All Publications
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The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.
JMIR medical informatics
2024; 12: e53787
Abstract
Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
View details for DOI 10.2196/53787
View details for PubMedID 38728687
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STAT: Mobile app helps clinicians manage inpatient emergencies at the bedside
HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION
2021; 9 (4): 100590
Abstract
In response to the unprecedented surge of patients with COVID-19, Massachusetts General Hospital created both repurposed and de-novo COVID-19 inpatient general medicine and intensive care units. The clinicians staffing these new services included those who typically worked in these care settings (e.g., medicine residents, hospitalists, intensivists), as well as others who typically practice in other care environments (e.g., re-deployed outpatient internists, medical subspecialists, and other physician specialties). These surge clinicians did not have extensive experience managing low frequency, high acuity emergencies, such as those that might result from COVID-19. Physician-innovators, in collaboration with key hospital stakeholders, developed a comprehensive strategy to design, develop, and distribute a digital health solution to address this problem. MGH STAT is an intuitive mobile application that empowers clinicians to respond to medical emergencies by providing immediate access to up-to-date clinical guidelines, consultants, and code-running tools at the point-of-care. 100% of surveyed physicians found STAT to be easy to use and would recommend it to others. Approximately 1100 clinicians have downloaded the app, and it continues to enjoy consistent use over a year after the initial COVID-19 surge. These results suggest that STAT has helped clinicians manage life threatening emergencies during and after the pandemic, although formal studies are necessary to evaluate its direct impact on patient care.
View details for DOI 10.1016/j.hjdsi.2021.100590
View details for Web of Science ID 000715127500002
View details for PubMedID 34700138
View details for PubMedCentralID PMC8536501