NYU Langone Health faculty, in collaboration with investigators from the Mayo Clinic, the University of B.C., and Kansas University Medical Center, aim to optimize the learning of visual diagnosis by developing a demonstration project using >80,000 ECGs downloaded from NYU Langone's Electronic Medical Record. The ECGs have been categorized in order to develop a generalizable method of presenting visual diagnosis cases that can provide efficient and effective learning. Target learners in the study were identified from a wide range of healthcare professionals including emergency physicians, internists, cardiologists, nurses and paramedics.
Scope of the Database: The database is described in detail here (link to MHSRS Abstract). In brief, the deidentified ECGs were collected from patients who visited the NYU Langone Medical Center Perelman Emergency Department over a five-year period.
Investigators
- NYU Langone Health:
- PI: Martin V Pusic
- Co-I: Marc M. Triola, MD
- Co-I: Silas Smith, MD
- Co-I: Barry Rosenzweig, MD
- Co-I: Jeffrey Lorin, MD
- Collaborator: David Gelman, MD
- Collaborator: Julie Friedman, MD
- Collaborator: David Rhee, MD
- Collaborator: Joseph Bennett, MD
- Collaborating:
- Co-I: Matthew Lineberry, PhD (University of Kansas)
- Co-I: Rose Hatala, MD (University of B.C.)
- Co-I: David Cook, MD (Mayo Clinic)
- Program Staff:
- Programmer: Mr. Eric Feng
- Program Coordinator: Ms. Greta Elysee
- Research Coordinator: Ms. Laura Penalo RN
- Grants Manager: Ms. Senem Suzek
- Instructional Design: Ms. So Young Oh
- Data Science: Mr. Ilan Reinstein