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The analysis attempted to describe the failures of each type of interaction, but there were some patterns of error that emerged in all types.
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The patterns of error identified in this study could be used to explore some specific educational strategies (as discussed above) designed to reduce error in acute care.
While big data looks for patterns of preferences, Soros often looks for patterns of error.
However there is growing concern that such methods are actually inconsistent with human behavior, based on observed patterns of error [8].
The numbers of errors in each category were used to identify patterns of error.
However, none of the individual patterns of error was in line with this suggestion.
This study used high-fidelity simulation to explore patterns of error in acute care.
However, the patterns of error rates for the real data are much more complex.
The identification of specific patterns of error may facilitate research-informed curriculum design and the development of tailored educational strategies.
Our analysis has revealed common patterns of approach to interpretation, but has also highlighted patterns of error shared by groups of participants.
The overall aim of this study was to identify patterns of error in acute care, which may subsequently be used to guide the development of targeted educational strategies.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com