Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Industrial Engineering

Committee Chair/Advisor

Greenstein, Joel S.

Committee Member

Gramopadhye , Anand K.

Committee Member

Watt , Paula J.


More than 200,000 in-hospital cardiac arrests are treated each year in the US with 21% survival rate. According to American Heart Association (AHA) guidelines, many causes for these arrests could be successfully treated if identified early. Such causes can be generalized as 'reversible causes'. Medical doctors identify the reversible causes associated with an arrest by recalling them from memory, using a mnemonic. In this study, using a cognitive aid such as an iPad application, the mnemonic was modified and causes were displayed alphabetically, and tested along with a new method that rank-ordered the reversible causes based on the patient context, known as the context-sensitive scheme. Both methods were implemented electronically in an iPad application and presented in a counterbalanced order to 11 anesthesia medical residents using simulated scenarios. Performance and usability measures were recorded and analyzed.
It took significantly longer for the participants to identify the reversible causes using the context-sensitive scheme. However, the scheme resulted in significantly lesser number of unnecessary keystrokes when compared to the alphabetical scheme. Some of these unnecessary keystrokes could affect the patient's outcome. Both the schemes agreed in terms of usability.
The above results indicate the potential of the context-sensitive scheme of the reversible causes to be useful when applied during an emergency scenario when refined further. A combination of both methods is suggested.



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