Automatic Patient Fall Outcome Extraction Using Narrative Incident Reports

Patient outcome is one of the key information categories in incident reporting. Being able to extract meaningful patient fall outcomes would allow better analysis of the consequences and possible mitigating actions for in-hospital fall incidents. This study aims to automate the extraction of patient outcomes from narrative fall incident reports by decomposing this into three classification subtasks: injured or not, injury types, and the number of injuries. Implementing an existing incident report classification (IRC) framework, the experimental results demonstrated that oversampling and structured features were effective to achieve better overall performances across all three subtasks. The study further validated the application of an IRC framework to deal with imbalanced classification problems found in fall patient outcome classification and advanced the science of automatic patient outcomes extraction.