BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT

San Francisco, CA
May 15-16, 2017
Vice President, Clinical Information
Advocate Health Care

Goel serves as inpatient CMIO for Advocate Health Care, a 12-hospital system based in Illinois. Before joining Advocate in early 2013, he was (ambulatory) associate director of Clinical Informatics at UC San Diego, and co-principal investigator for the San Diego Beacon Collaborative, where he helped build a regional HIE. Goel is board-certified in Internal Medicine and Clinical Informatics, and has been an examiner for the Baldrige Performance Excellence Program since 2014. He regularly publishes a blog focusing on topics such as using natural language processing to identify themes within EMRs to help clinicians and patients make more informed decisions, balancing clinical workflow standardization with identifying innovations through practice variation, and optimizing clinical information systems across care settings.

May 16, 2017
2:40pm - 3:15pm
Grand Ballroom

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. 

Key takeaways: 

  • NLP can help identify specific clinical conditions before formal diagnosis.
  • NLP is vulnerable to bias based on what clinicians enter in their documentation.
  • NLP can reduce human review of clinical documentation to achieve specific clinical outcomes.

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