Design concepts underlying the use of an expert system to teach diagnostic reasoning for antibody identification

摘要

Antibody identification is a laboratory task where medical technologists must select tests to run and interpret the results in order to determine the antibodies in a patient's blood. It has the classical characteristics of an abduction task, including masking and problems with noisy data. Based on a cognitive analysis of both successful and error-producing performances, a tutoring system (the transfusion medicine tutor, TMT), was developed that uses expert systems technology to provide immediate, context-sensitive feedback as students solve actual patient cases. Development of this system required consideration of aspects of artificial intelligence, education, psychology, and human factors engineering, as well as the domain of study (i.e., allo-antibody identification). In a formal field evaluation, when used by an instructor as a tool to assist with tutoring in a class laboratory setting, use of TMT resulted in improvements in antibody identification performance of 87-93% (p>.001) as compared to a passive control version which improved performance by 20%.