Andrew Lee Chu, MD

All Publications

• An Advanced Cardiac Life Support Application Improves Performance during Simulated Cardiac Arrest APPLIED CLINICAL INFORMATICS Senter-Zapata, M., Neel, D. V., Colocci, I., Alblooshi, A., AlRadini, F. M., Quach, B., Lyon, S., Coll, M., Chu, A., Rainer, K. W., Waters, B., Baugh, C. W., Dias, R. D., Zhang, H., Eyre, A., Isselbacher, E., Conley, J., Carlile, N. 2024; 15 (04): 798-807

  Abstract

   Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial. Forty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards (N = 22) or the guided app (N = 24). The primary outcome was successful return of spontaneous circulation (ROSC). Secondary outcomes included code leader stress and confidence, AHA ACLS guideline adherence, and errors. A focus group of 22 residents provided feedback. Statistical analysis included two-sided t-tests and Fisher's exact tests. App users showed significantly higher ROSC rate (50 vs. 18%; p = 0.024), correct thrombolytic administration (54 vs. 23%; p = 0.029), backboard use (96 vs. 27%; p < 0.001), end-tidal CO2 monitoring (58 vs. 27%; p = 0.033), and confidence compared with baseline (1.0 vs 0.3; p = 0.005) compared with controls. A focus group of 22 residents indicated unanimous willingness to use the app, with 82% preferring it over AHA pocket cards. Our guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice.

  View details for DOI 10.1055/s-0044-1788979

  View details for Web of Science ID 001326831600002

  View details for PubMedID 39357878

  View details for PubMedCentralID PMC11446628

• The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR medical informatics Preiksaitis, C., Ashenburg, N., Bunney, G., Chu, A., Kabeer, R., Riley, F., Ribeira, R., Rose, C. 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

• STAT: Mobile app helps clinicians manage inpatient emergencies at the bedside HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION Chu, A. L., Ziperstein, J. C., Niccum, B. A., Joice, M. G., Isselbacher, E. M., Conley, J. 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