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HOW ‘ARTIFICIAL INTELLIGENCE’ IS CHANGING HEALTH CARE FOR PATIENTS

How AI can decrease cost of care, while improving patient outcomes
By  Gina DiPietro and Maddie Ellis
September 14, 2021

The term “artificial intelligence” may conjure up futuristic images of flying
cars or talking robots, but the way it’s used is more practical than what we see
in the movies.

Dr. Eric Eskioglu

“Think about the sensors on your car that beep when someone is in your blind
spot. Or how you interact with your Alexa and other devices that learn our
behaviors over time. That’s AI at work,” said Dr. Eric Eskioglu, Novant Health
executive vice president and chief medical officer.

Health care systems are also putting AI to good use. And its potential to
improve the health of our communities is only expected to increase, Eskioglu
said.

He explained what makes this so exciting, both now and in the future, to Healthy
Headlines’ Gina DiPietro. Listen to their conversation in its entirety on the
Novant Health Industry Insights podcast channel.


HOW IS AI USED IN HEALTH CARE?

Let’s start with the question: What is data? In health care, data includes
anything a patient generates throughout their lifetime – information from annual
physicals, sick visits, ultrasounds, and prior hospitalizations or surgeries.
This is important because we need vast amounts of data from diverse groups of
people to use AI efficiently.

In some cases, patient data generates “predictive analytics,” which is another
piece of the AI puzzle. Predictive analytics help us identify trends that, for
example, could result in less lab work, fewer X-rays, or the ability to predict
health issues before they happen. This has the potential to eliminate
unnecessary costs and keep more people out of the hospital.

Currently, we’re doubling our amount of medical knowledge about every 70 days.
And that’s going to increase to every 30 days. So, it’s imperative that we put
this data to good use.




WILL AI REPLACE MY DOCTOR OR NURSE?

No. AI is not going to replace physicians and nurses. You simply can’t replace a
quality like human empathy. What I do foresee is that physicians and nurses who
fully embrace AI will outperform those who do not. They’ll be best equipped to
care for patients with the help of things like predictive analytics.

I think we’ll see its biggest impact on primary care. Those physicians have the
longest tenure with their patients. Sometimes, they have 30 or 40 years of
information about someone’s medical history. That’s valuable data and AI will
help us put it to good use.


HOW ELSE MIGHT AI REDUCE COSTS FOR PATIENTS?

Here’s an example: We've been looking at uncomplicated pneumonia admissions.
Most people recover and their outcomes are similar. But we’ve noticed that some
physicians order more tests than others, which can drive up the cost of care.

Some physicians order the “kitchen sink” or “the Cadillac workup,” meaning they
request daily labs or daily X-rays for their pneumonia patients. But other
physicians might request those tests every two or three days. AI will help us
reduce this variation among providers by cutting back on unnecessary testing and
ultimately, reducing costs.


WHAT ELSE EXCITES YOU ABOUT AI?

I believe that AI will be a great equalizer for health equity. We’ve been very
successful working with a startup company called Viz.ai. We were one of the
first in the country to adopt it when it was just at the beginning stages, and
it's proven to be a hit. Patients have been true winners.

Viz.ai merges artificial intelligence with telemedicine. It allows us to detect
a stroke much faster –especially for rural patients who, historically, have not
had quick access to stroke care. On average, we've been able to save about 10
minutes per stroke patient, which equates to about 19 million brain cells.
Quality of life has increased, while the length of hospital stays has decreased.


PREVENTING BIAS IN AI ALGORITHMS

I’d like to mention one caution point. We must ensure that bias is not entered
into AI algorithms. We need to make sure we have data from all populations –
including people from indigenous communities, and those who are white, Black,
Asian American, Latino, Chinese, etc. – because only then we can get a true
picture of how to improve outcomes for patients.

Tags: artificial intelligence, AI, machine learning, predictive analytics,
clinical variation


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