BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT
SAN FRANCISCO, CA - MAY 15-16, 2017
Identifying patients with a stroke as early as possible can help health systems initiate effective treatments in a timely fashion. Unfortunately, patients with ambiguous presentations can delay a stroke diagnosis and subsequent treatment. Some healthcare systems manually review patient records to determine if a patient had a stroke that was missed by the primary team. Electronic medical records allow for computer review of hundreds of charts with Natural Language Processing (NLP) to identify those patients whose presentation merit further evaluation for stroke. We describe one healthcare system’s approach to use NLP to reduce the manual review required to identify patients who may have suffered a stroke undetected by the primary team.
The process occurs in near-real time as notes are reviewed on a daily basis. The stroke detection algorithm is updated regularly as the human reviewers provide feedback around identified cases.
The project highlights NLP’s ability to provide near real-time analytics and process efficiencies for this specific use case with potential applications for other disease states.