EMIC Fellowship
Networking, mentorship, and the opportunity to work on pressing issues in acute care and healthcare innovation.
Mentorship from leaders in the field: work one-on-one with EMIC fellowship directors and tap into a network of physicians and industry innovators shaping the future of acute care.
A real project, start to finish: spend a year turning an idea into a tangible outcome, whether an MVP, a published paper, a conference presentation, or a device ready for its next stage.
A community of builders: join a small, selective cohort of trainees, clinicians, and industry professionals focused on innovating healthcare delivery.
Requirements
Meet monthly with assigned fellowship director
Submit quarterly status updates
Attend/present at ACEP if applicable to the project stage
Deliver a final presentation to EMIC leadership in December
Participate in ad-hoc EMIC activities (resource hub, events, blog) as capacity allows
Innovation Lab fellow: contribute a defined component toward building the EMIC Innovation Lab
Additional requirements and information can be found in the syllabus
Who
Open to all medical professionals, from students to attendings, as well as industry professionals interested in healthcare innovation.
What
Year-long fellowship program engaging medical professionals interested in solving pressing problems in acute care.
Where
Remote from any location.
When
Application deadline is January 15;
fellowship begins February 15.
Current Fellow
Institution: Kern Medical
Interests: Medical education, improvisation in under resourced healthcare settings, AI for early disease detection and diagnostic assistance
Past Fellows
Institution: Stanford EM
Projects: AI 4 EM; How to Introduce Your Colleagues to the Benefits of AI in the Clinical Setting
Institution: Loma Linda University
Projects: Abdominal Aortic Aneurysm Simulation Model; Median Nerve Block Phantom Model
Institution: Columbia University
Projects: EMIC Fellowship Development;
Innovation Opportunity Directory
Highlighted Fellow Projects
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AI4EM
A resource hub for materials related to AI in healthcare and its applications to EM (Connor Grant, MD)
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Ultrasound Education Models
Inexpensive and easy to construct high-fidelity ultrasound models (Lauren Sinsioco, MD)