Identifying Harm Events in Clinical Care through Medical Narratives
This work addresses the challenge of large-scale analysis of patient safety reports for healthcare systems, though it appears incremental in method.
The paper tackled the problem of identifying harm events in clinical care from medical narratives, presenting a method based on attentive convolutional and recurrent networks that significantly improves performance over existing methods.
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.