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Accession Number:
AD1093635
Title:
An Adaptive Tutor for Improving Visual Diagnosis
Descriptive Note:
Technical Report,30 Sep 2017,29 Sep 2018
Corporate Author:
New York University New York United States
Report Date:
2018-10-01
Pagination or Media Count:
18.0
Abstract:
Online cognitive trainers for visual diagnosis can transcend institutional barriers to enable broad distribution of learning material. However, most current examples are based on declarative knowledge instructional designs that deliver outcomes that are only indirectly connected to patient care. Our key contention is that cognitive learning platforms, using evidence-based instructional designs, can facilitate efficient and effective visual diagnosis skill development and maintenance. Progress to date We have had success in three key components of the eventual adaptive tutor 1 we have assembled a corpus of 80,000 ECGs with their associated clinical information and have organized that corpus into 2 a prototype presentation database that allows any stakeholder to search and download ECGs according to any of the 120 American Heart Association diagnostic labels. 3 We have completed three pilot studies designed to inform the design of the adaptive tutor including a focus groups to develop a relative importance ranking, b pairwise comparisons by cardiologists to determine the feasibility of complexity ranking of ECGs and c exploring the degree to which two overlapping ECG phenotypes can be confused and how this can be statistically modeled. We are now well positioned to use these materials and methods to carry out the next phase, a large prospective data collection and subsequent impact trial of adaptive learning.
Distribution Statement:
APPROVED FOR PUBLIC RELEASE
