Principal Investigator: Martin V. Pusic, MD, PhD

An Adaptive Tutor for Improving Visual Diagnosis, a two-year project funded by the U.S. Department of Defense, aimed to create an adaptive tutor that determines the baseline proficiency of individuals interpreting electrocardiograms, or ECGs, and then tailored case-based learning until reliable competency was achieved.

The research project had several objectives:

• to assemble an online ECG library from authentic field cases collected from an emergency department
• to develop ontologic and statistical models of the ECG cases to inform the rational design of the adaptive learning system
• to develop an evidence-based learning adaptation algorithm to ensure efficient and reliable development of skills at scale

NYU Langone Co-Investigators

Jennifer Hill, PhD
Jeffrey Lorin, MD
Barry Rosenzweig, MD
Silas Smith, MD
Marc Triola, MD

Collaborative Co-Investigators

David Cook, MD, Mayo Clinic
Rose Hatala, MD, University of British Columbia
Matthew Lineberry, PhD, University of Kansas Medical Center

Project Personnel

Greta Elysée, Program Coordinator
Eric Feng, Programmer
Ilan Reinstein, Data Scientist