Identifying Harm Events in Clinical Care through Medical Narratives

TL;DR

This paper introduces a deep learning approach using attentive convolutional and recurrent networks to analyze medical narratives, aiming to identify and categorize harm events in clinical care more effectively than previous methods.

Contribution

The study presents a novel deep learning framework that improves the detection and categorization of harm events in clinical narratives compared to existing techniques.

Findings

Significant performance improvement over previous methods

Effective categorization of harm severity levels

Demonstrated applicability to large-scale clinical narratives

Abstract

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.