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A994
October 14, 2007
3:30 PM - 5:00 PM
Room Room 123
Transparent Reality Simulation Enhances Learning of Anesthesia Machine Function and Dynamics
Samsun Lampotang, Ph.D., Cynthia E. Kaschub, M.S., David E. Lizdas, B.S.M.E., Ira Fischler, Ph.D.
Anesthesiology, University of Florida, Gainesville, Florida
Introduction: The Virtual Anesthesia Machine (VAM) simulation is a free, interactive learning tool available on the web since 1999 [1, 2]. It is characterized by a simplified pneumatic circuit and visible, color-coded gas molecule icons whose flow through the plumbing is altered in real time in response to user interventions. This transparent reality simulation technique used by VAM renders the invisible visible, providing a mental model designed to make the abstract concrete and the complex simple to visualize and understand [3]. In contrast, traditional "black box" simulations mimic the inputs and outputs of a system with the internal functions and processes remaining an opaque black box, hidden from learners. We compared learning outcomes with a transparent vs. opaque simulation of the same anesthesia machine.

Methods: Implementing the black box simulation. Using an identical simulation engine (mathematical models and scripts) to the transparent reality VAM, we created an opaque simulation of an anesthesia machine with Director (Adobe Systems Inc, San Jose, CA). The opaque simulation was externalized via a photographic image of a Modulus II (Ohmeda, Madison, WI) anesthesia machine taken from a typical anesthesia provider perspective. All user adjustable components such as the ventilator controls and oxygen flush were made interactive, acting as inputs to the simulation engine. Only outputs visible to the naked eye such as movement of the bellows and pressure gauge needles were reproduced. Study protocol. With prior IRB approval, 40 pre-health profession undergraduate students with no prior knowledge of anesthesia machines were divided into 2 groups and underwent a single, one-hour learning session, using either the transparent or opaque simulation. The following day, their knowledge of machine components, function and dynamics was tested.

Results: On 2 of 3 major measures of retention and transfer, the transparent group performed significantly better. The groups were comparable in their ability to name machine components, but the transparent group provided better and more complete explanations of component function (p<0.005) and was more accurate in remembering and inferring cause-and-effect dynamics of the machine and relations among components (p<0.02).

Discussion: Misuse was three times more common than equipment failure in closed claims (most due to death and permanent brain damage) associated with gas delivery equipment [4]. Effective educational and training techniques may have the most potential to reduce human error and improve the safety of anesthesia equipment.

References

1. http://vam.anest.ufl.edu/wip.html

2. US Patent 7,128,578

3. Educational Technology 46(1)55-59, 2006

4. Anesthesiology 87:741-8, 1997.[figure1][figure2]

Anesthesiology 2007; 107: A994
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