  Preventable medical errors are estimated to be among the leading causes of
injury and death in the United States. To prevent such errors, healthcare
systems have implemented patient safety and incident reporting systems. These
systems enable clinicians to report unsafe conditions and cases where patients
have been harmed due to errors in medical care. These reports are narratives in
natural language and while they provide detailed information about the
situation, it is non-trivial to perform large scale analysis for identifying
common causes of errors and harm to the patients. In this work, we present a
method based on attentive convolutional and recurrent networks for identifying
harm events in patient care and categorize the harm based on its severity
level. We demonstrate that our methods can significantly improve the
performance over existing methods in identifying harm in clinical care.
