Expert Systems Based Clinical Assessment and Tutorial Project

This project at the Texas College of Osteopathic Medicine (Fort Worth) evaluated the use of an artificial-intelligence-derived measure, "Knowledge-Based Inference Tool" (KBIT), as the basis for assessing medical students' diagnostic capabilities and designing instruction to improve di...

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Bibliographic Details
Main Authors Papa, Frank, Shores, Jay
Format Report
LanguageEnglish
Published 30.08.1992
Subjects
Artificial Intelligence
Clinical Diagnosis
Diseases
Evaluation Methods
Expert Novice Problem Solving
Expert Systems
Higher Education
Instructional Effectiveness
Knowledge Level
Medical Education
Problem Solving
Student Evaluation
Symptoms (Individual Disorders)
Test Reliability
Test Validity
Texas College of Osteopathic Medicine
Tutorial Programs
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Summary:This project at the Texas College of Osteopathic Medicine (Fort Worth) evaluated the use of an artificial-intelligence-derived measure, "Knowledge-Based Inference Tool" (KBIT), as the basis for assessing medical students' diagnostic capabilities and designing instruction to improve diagnostic skills. The instrument was designed to address the problem that, in medicine, diagnostic expertise is problem-specific and appears to be more a factor of the student's knowledge base than cognitive skills. This study determined that the KBIT produced reliable and valid (based on comparisons of diagnostic accuracy of experts with those of novices) for four different problem areas: Weakness, Red Eye, Papulosquamous Disorders, and Elevated Creatinine. Additionally the study showed that two expert/KBIT-derived instructional approaches significantly improved the diagnostic accuracy of treatment student groups when compared to a control group and to students conventionally trained. After the executive summary and a project overview, this report describes the project's background and origins, its components and activities, and results. Attached is a related article titled "An Expert Program Shell Designed for Extracting Disease Prototypes' and Their Use as Models for Exploring the 'Strong Problem-Solving Methods' Employed in Clinical Reasoning" (F.J. Papa; S. Meyer). (DB)