Dynamic simulation of medical diagnosis: learning in the medical decision making and learning environment MEDIC

Authors:
Cleotilde Gonzalez;Colleen Vrbin
Affiliations:
Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, Pennsylvania;Center for Pathology Quality and Healthcare Research, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
Venue:
USAB'07 Proceedings of the 3rd Human-computer interaction and usability engineering of the Austrian computer society conference on HCI and usability for medicine and health care
Year:
2007

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Abstract

MEDIC is a dynamic decision making simulation incorporating time constraints, multiple and delayed feedback and repeated decisions. This tool was developed to study cognition and dynamic decision making in medical diagnosis. MEDIC allows one to study several crucial facets of complex medical decision making while also being well controlled for experimental purposes. Using MEDIC, there is a correct diagnosis for the patient, which provides both outcome and process measures of good performance. MEDIC also allows us to calculate cue diagnosticity and probability functions over the set of hypotheses that participants are explicitly considering, based on assumptions of local (bounded) rationality. MEDIC has served in a series of studies aimed at understanding learning in dynamic and real-time medical diagnotic situations. In this paper, we outline the tool and highlight results from these preliminary studies which set out to measure learning.