The Emergent Features of a Medical Object Display Improve Anesthesiologists' Performance of Simulated Diagnostic Tasks

• Published in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2000, Vol.44 (26), p.258-261
• Main Authors: Blike, George, Jensen, Jens, Whalen, Kate, Surgenor, Stephen
• Format: Review
• Language: English
• ISSN: 1541-9312; 1071-1813; 2169-5067
• DOI: 10.1177/154193120004402632

The anesthesiologist is expected to monitor multiple data streams from the anesthetized patient, recognize problem states, identify the etiology or cause of problem states (i.e., perform diagnosis) and take corrective action to return a patient to the desired goal state. This study represents efforts to further investigate features of a multi-dimensional Object display of hemodynamic data. Clinicians using this Object display recognized problem states and determined etiology, faster and more accurately than when they used a Single Sensor Single Indicator (SSSI) Numeric display. This study hypothesized that shapes designed into the Object display accounted for the diagnostic performance gain observed. To test this hypothesis, variant of the Object display, with the emergent shape features removed (Object Minus Shapes display) was used as a probe to assess the impact of the shape-encoded information on diagnostic performance. Both the Object and Object Minus Shapes displays improved problem state recognition when compared to the Numeric SSSI display. The former displays graphically represent current data values and boundary information using a pointer, a reference scale, and solid bars to depict normal and abnormal value ranges. The emergent shape features in the Object display were the essential graphical elements that conferred improved etiology determination by anesthesiologists. This study partially validates the design goal used to construct the Object display under investigation—to map multi-dimensional physiologic relationships of hemodynamic data using graphical representations with emergent features that are clinically meaningful.