An Adaptive Tutor for Improving Visual Diagnosis

Institute for Innovations in Medical Education Grants An Adaptive Tutor for Improving Visual Diagnosis
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Institute for Innovations in Medical Education Grants An Adaptive Tutor for Improving Visual Diagnosis

Principal Investigator: Martin V. Pusic, MD, PhD

This two-year project, funded by the U.S. Department of Defense, aims to create an adaptive tutor that determines the baseline proficiency of individuals interpreting electrocardiograms, or ECGs, and then tailors case-based learning until reliable competency is achieved.

The research project has several aims:

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

NYU 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
Jacqueline Gutman, Data Scientist
Sidrah Malik, Project Manager

 

By the Numbers
80,000

diagnostic ECG cases to be added to an online library

>450

healthcare professionals, including physicians, residents, and more, to be recruited

4

medical schools collaborating on this project (3 in the United States and 1 in Canada)

ECG Rotation Learning Curve