Process Deviation Assessment
Many medical processes are plagued by human faults. To assist in addressing this issue, we are researching a method for automatically recognizing when performers of a medical procedure stray from the allowed ways of executing that process as indicated by a thorough process model.
The process models are used to produce sample process executions, which are subsequently seeded with synthetic mistakes. The process models depict the coordination of actions of various process actors in both normal and abnormal settings. The test findings indicate that the suggested technique might be used in healthcare settings to assist detect mistakes before they cause harm.
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