Bio

Austin Schoeffler, M.D., is an emergency medicine physician and clinical informatics fellow at Stanford University. Dr. Schoeffler earned his M.D. from The Ohio State University College of Medicine and completed his Emergency Medicine Residency at University Hospitals/Case Western Reserve University in Cleveland. He is currently pursuing a two-year fellowship in Clinical Informatics at Stanford, focusing on the integration of machine learning and digital health solutions within emergency care.

Dr. Schoeffler has a strong background in both clinical operations and digital innovation. He has assisted on projects leveraging AI-driven facial recognition software for depression screening in the emergency department, and is currently critically evaluating the impact of ambient AI scribes on clinical care and helping to create the first AI benchmark for emergency medicine. His operational experience includes governance and workflow optimization at his previous institution, where he contributed to initiatives enhancing patient care delivery and hospital efficiency.

His scholarly interests center on responsible AI integration, innovation, building the future of digital health technology, and expanding access to populations not traditionally reached by existing clinical infrastructure. He is committed to fostering industry-academic partnerships, rigorously evaluating emerging AI tools, and benchmarking AI products for deployment in acute care settings. Clinically, he is passionate about evidence-based care, digital health, and the development of novel care delivery models in emergency medicine.

Clinical Focus

  • Fellow
  • Emergency Medicine
  • Clinical Informatics
  • Machine Learning
  • AI Equity
  • Innovation
  • Clinical Decision Support Systems

Boards, Advisory Committees, Professional Organizations

  • Member, American College of Emergency Physicians (2022 - Present)
  • Member, American Medical Informatics Association (2024 - Present)
  • Member, Society for Academic Emergency Medicine (2022 - Present)

Professional Education

  • Fellowship, Stanford Healthcare, Clinical Informatics
  • Residency, University Hospitals/Case Western Reserve University (2025)
  • MD, The Ohio State University College of Medicine (2022)

All Publications

  • Will Artificial Intelligence Replace Me? Automation Susceptibility of Emergency Physician Tasks. Annals of emergency medicine Rose, C., Molins, E., Schoeffler, A., Kabeer, R., Frank, M. R., Preiksaitis, C. 2026

    Abstract

    To apply Autor's labor economics task framework to classify emergency physician tasks by automation susceptibility and map current artificial intelligence (AI) capabilities to each category.We synthesized 6 published time-motion studies, ACGME Core Entrustable Professional Activities, and the O∗NET emergency physician task inventory into a unified list of 14 task categories. Two board-certified emergency physicians independently classified each task using Autor's 4-category framework. Current AI capabilities were mapped to each task using a 3-tier schema: Replace, Augment, or No Current Application.Nine tasks (64.3%) were classified as nonroutine abstract, 3 (21.4%) as routine cognitive, and 2 (14.3%) as nonroutine manual. No tasks were Routine Manual. AI replacement is concentrated in routine cognitive tasks (documentation, medical records review, emergency department operations management), which consume 20% to 40% of physician shift time. Augmentation dominates in nonroutine abstract domains. Nonroutine manual tasks show minimal AI penetration.Routine cognitive tasks consume a disproportionate share of emergency physician shift time, making them the immediate target for AI-driven workflow restructuring. Beyond this, augmentation of nonroutine abstract tasks is accelerating, warranting ongoing reassessment of automation boundaries across all task categories. As AI capabilities continue to expand, structured task-level analyses of this kind will be essential for anticipating workforce needs and informing AI implementation strategy, residency training design, and physician preparation in emergency medicine.

    View details for DOI 10.1016/j.annemergmed.2026.06.031

    View details for PubMedID 42687470

  • Toward a test of medical AI superintelligence. Nature medicine Goh, E., Wu, D., Walton, C., McCoy, L. G., Perez, A., Wegner, L., Nateghi Haredasht, F., Luo, L., Lacar, K., Buckley, T., Schoeffler, A., Brodeur, P., Black, K. C., Havlik, J., Liu, Y., Chen, P. C., Schaekermann, M., Rumsfeld, J., Lopez-Martinez, D., Maeder-York, P., Singhal, K., Gunning, D., Ku, B., Warraich, H., Nundy, S., Ravi, V., Milstein, A., Hom, J., Schulman, K., Rajpurkar, P., Manrai, A. K., Wachter, R., Topol, E., Horvitz, E., Rodman, A., Chen, J. H. 2026

    View details for DOI 10.1038/s41591-026-04539-8

    View details for PubMedID 42509372

    View details for PubMedCentralID 11519755

  • Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study. JMIR AI Preiksaitis, C., Alvarez, A., Winkel, M., Karamatsu, M., Brown, I., Sama, N., Morris, L., Lee, J. Y., Gubbels, A., Wahl, E., Frye, A., Schoeffler, A., Gharahbaghian, L., Rose, C. 2026; 5: e92193

    Abstract

    Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising approach to reduce this burden, but objective evidence in the emergency department (ED) setting remains limited, and prior reports have been constrained by short observation windows and low adoption.This study aimed to evaluate the association between ambient AI scribe use and on-shift documentation time during a 13-month staged rollout in a busy ED, accounting for physician- and patient-level factors.We conducted a retrospective cohort study at a tertiary academic ED from February 2025 to March 2026. The analytic cohort comprised 10,344 encounters managed by 100 attending physicians across 4 ED care settings. We restricted analysis to encounters managed by a single attending physician and excluded those with human scribes. The comparison group comprised encounters in which the ambient AI scribe was not used; use was determined entirely at attending physician discretion on an encounter-by-encounter basis. The primary outcome was on-shift documentation time derived from electronic health record audit logs. We used mixed-effects linear models with physician random intercepts to adjust for patient and encounter characteristics.Ambient AI scribe use was associated with a 72.6-second reduction in on-shift documentation time per encounter (95% CI 63.8-81.4; P<.001). The effect was similar in magnitude for high-use physicians (use rates of ≥18.2%, which was the cohort mean; -71.6 seconds) and low or moderate users (-64.2 seconds), with no statistically significant difference (P=.51). Note character count decreased by 690 characters (95% CI 273-1107; P=.001); after-shift documentation time increased modestly by 9.1 seconds (95% CI 2.9-15.3; P=.004). Negative control outcomes were largely null, and a within-clinician placebo permutation test yielded a distribution centered at 0 (mean -0.8 seconds), inconsistent with the observed effect arising from confounding alone.In this single-center analysis, ambient AI scribe use was associated with a statistically significant reduction in on-shift documentation time (P<.001), equivalent to approximately 24 minutes per 8-hour shift if used across 20 encounters. These findings extend prior descriptive work with adjusted inferential evidence and support the clinical relevance of ambient AI scribes for ED documentation burden, although the magnitude of benefit varies by physician, patient, and workflow factors.

    View details for DOI 10.2196/92193

    View details for PubMedID 42391625