BACKGROUND
Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records as it is often available only in unstructured text.
OBJECTIVE
Assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs (VA) across disparate healthcare facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.
METHODS
Clinical documents related to arboviral disease were annotated following selection using a semi-automated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated involving machine learning and neural language models for extraction accuracy.
RESULTS
Among annotated instances, 2,659 (58%) contained an affirmed mention of travel history while 347 (7.6%) were negated. Inter-annotator agreement resulted in a document-level Cohen’s kappa (Κc) of 0.776. Automated text processing accuracy (F1=85.6) and computational burden were acceptable such that the system can provide a rapid screen for public health events.
CONCLUSIONS
Automated extraction of patient travel history from clinical documents is feasible for enhanced capabilities to improve public health systems. This evaluation was initially performed on emergent arboviral disease. More recently, this system was utilized in early phases of response to COVID-19 in the United States although its utility was limited to a relatively brief window due to rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.
Neuroinvasive Arboviral Disease in the United States: 2003 to 2012