In previous work, we have developed, deployed and evaluated a novel intelligent medical training system for pathologic diagnosis and reporting. SlideTutor is an individualized, adaptive, simulation environment that provides explanations and assistance specific to each student's needs. Our results show that the system produces dramatic improvements in diagnostic and reporting accuracy. On average, students achieve a 400% gain in performance, and retain these skills over time. A unique aspect of our system is that it dynamically models student skills, knowledge and misconceptions, so that it can adapt its interventions. In the process, the SlideTutor system captures an enormous amount of information about the intermediate steps in the physician reasoning. We have developed an elaborate research infrastructure that allows us to make use of this abundant information as research data. With this novel research infrastructure, we are poised to make far more general statements about how to create adaptive and individualized systems for training physicians. We now propose to leverage the SlideTutor infrastructure to focus on three foundational areas where there is nearly no previous research in medical domains: metacognition, performance prediction, and learning behaviors. Work in these areas has the potential to deeply impact the fields of patient safety, medical simulation and competency- based assessment, in addition to guiding the development of future medical training systems.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
3R01LM007891-05S1
Application #
8089164
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2009-07-01
Project End
2010-06-30
Budget Start
2009-07-01
Budget End
2010-06-30
Support Year
5
Fiscal Year
2010
Total Cost
$100,000
Indirect Cost
Name
University of Pittsburgh
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Feyzi-Behnagh, Reza; Azevedo, Roger; Legowski, Elizabeth et al. (2014) METACOGNITIVE SCAFFOLDS IMPROVE SELF-JUDGMENTS OF ACCURACY IN A MEDICAL INTELLIGENT TUTORING SYSTEM. Instr Sci 42:159-181
Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga et al. (2013) Automated detection of heuristics and biases among pathologists in a computer-based system. Adv Health Sci Educ Theory Pract 18:343-63
Mello-Thoms, Claudia; Mello, Carlos A B; Medvedeva, Olga et al. (2012) Perceptual analysis of the reading of dermatopathology virtual slides by pathology residents. Arch Pathol Lab Med 136:551-62
El Saadawi, Gilan M; Azevedo, Roger; Castine, Melissa et al. (2010) Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold. Adv Health Sci Educ Theory Pract 15:9-30
Payne, Velma L; Medvedeva, Olga; Legowski, Elizabeth et al. (2009) Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Artif Intell Med 47:175-97
El Saadawi, Gilan M; Tseytlin, Eugene; Legowski, Elizabeth et al. (2008) A natural language intelligent tutoring system for training pathologists: implementation and evaluation. Adv Health Sci Educ Theory Pract 13:709-22
Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga et al. (2007) Evaluation of an intelligent tutoring system in pathology: effects of external representation on performance gains, metacognition, and acceptance. J Am Med Inform Assoc 14:182-90
Crowley, Rebecca S; Medvedeva, Olga (2006) An intelligent tutoring system for visual classification problem solving. Artif Intell Med 36:85-117
Saadawi, Gilan M; Legowski, Elizabeth; Medvedeva, Olga et al. (2005) A method for automated detection of usability problems from client user interface events. AMIA Annu Symp Proc :654-8
Crowley, Rebecca S; Medvedeva, Olga (2003) A general architecture for intelligent tutoring of diagnostic classification problem solving. AMIA Annu Symp Proc :185-9