Hospital Case Management Optimization

[Narrator]

A patient goes to the hospital with chest pain and ends up staying for a day and a half. Does that count as an "inpatient" stay or as "observation?" When hospitals have to review patient cases for medical necessity, case managers and physician advisors have to do the work manually, a time consuming and often error-prone process. So how can hospitals perform case reviews in a more accurate and efficient way? The answer is clinically aware artificial intelligence, delivered by Optum Case Advisor. Our experts start with data, over 15 million real-life medical necessity case reviews, and apply deep learning techniques to teach our technology systems how to quickly and accurately recognize cases that require a physician advisor's review and those that don't. We combine these deep learning models with natural language processing capabilities, or NLP, so our technology can read patients' clinical documentation and quickly identify the clinical risk factors needed to determine medical necessity. Hours of manual analysis are done in just seconds. Every Case Advisor review also includes citations to our vast library of thousands of peer-reviewed articles about medical necessity. By citing the most relevant ones, Case Advisor improves the defensibility of every patient status determination. And we can check through 100% of the hospital's cases. With clinically aware AI like deep learning and NLP added to workflow technologies, medical necessity reviews become more efficient, reimbursement is more accurate, and case managers and clinicians spend more time focusing on their patients and other critical tasks. It's another example of how Optum is moving health care forward.