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Although there has been a decrease in the number of anesthesia-related critical incidents, there are still opportunities for further improvement. We discuss the potential of integrated monitoring and artificial neural networks as a means of vigilantly watching for patterns in multiple variables to detect incidents and reduce false alarms. We estimate that half the anesthesia-related events could be detected with integrated monitoring using only 5 variables. A review of research using artificial intelligence/expert systems indicates limited potential for success using these tools alone for integrated monitoring in the operating room. We present artificial neural networks as an approach that is more suited to the type of multivariable monitoring and pattern recognition required. Along with rule-based artificial intelligence, these now have the potential to help develop innovative monitoring in the operating room.

To bridge a gap we perceive to exist between the traditionally educated engineer, who is skilled primarily in high tech, and the broader trained engineer being called for by industry, we have developed a senior level course entitled Professional Preparation. This course is built around a group design project, but also includes exposure to a wide variety of needed skills such as problem solving, creative thinking, ethics, writing to learn, producing documentation and making oral and written reports. Student and employer reaction to the course is uniformly enthusiastic. Data collected from a survey of over 400 of our graduates shows a marked difference in their perception of the importance of and their educational preparation in specific skills with additional differences between those who took this course and those who did not.

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