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.