Matt Fornito, Head of Artificial Intelligence at Trace3, and host Bill Russell discussed how an organization can launch a successful AI program.
Fornito explained that AI is an umbrella term; it can refer to data science, machine learning, or deep learning, and it help identify patterns and clusters for businesses. As head of AI at Trace3, Fornito focuses on educating their partners about the potential of AI and what it can do for their organizations.
Across industries, AI is being utilized at a much greater rate than it was initially in 2011, according to Fornito. It can be used to relieve people of completing mundane tasks, and in other industries, it is used for fraud detection and facial recognition.
Now, health insurance companies are heavily investing in AI so that claims can be more accurately and quickly approved. In hospital systems, they each vary in progression, according to Fornito. For example, the manual work in billing scales linearly and includes more labor. The process of deep learning is advancing to use computer vision, reading pages and using natural language processing to identify prognoses and applicable billing codes.
“It's really going across the spectrum for the organizations that are moving further and further along in their AI journey,” he said.
A new advancement is AI beginning to support patients and providers by creating a personalized healthcare experience through software applications and models of x-rays and MRIs to detect anomalies.
“At the end of the day, or at the end of this light of the tunnel, what we want to do is ultimately stop treating the disease and treat that patient holistically. Suppose we can do that using augmentation through AI. In that case, we should be able to reduce costs, reduce the number of treatments and other associations for diagnosis and get a patient in and out the door faster and happier,” he said.
Fornito created a framework theory, a model built on five levels of AI maturity. The first level is an organization with no data scientists and very little understanding of the process. However, they are businesses that want to do more; understanding predictions, data insights, and value.
Different clusters distinguish levels two through four in the journey. There are more data science teams that see value revenue and cost savings come from their models. According to Fornito, by level two, models should be paying for team infrastructure and software applications.
“All the revenue that that group is making should pay for itself because in all the analyses I've done, and from what I seen from other research teams, the value of every dollar invested in AI is conservatively five-to-ten-x and up to 30x for every dollar spent,” he explained.
At the highest level, where businesses like Facebook and Amazon are, companies innovate and renovate society. They employ hundreds of data scientists and have an executive-level champion promoting it and the value of the data and models.
According to Fornito, organizations can start at different levels, but it takes a higher-level AI champion and budget.
“AI is fortunately, or unfortunately, not a cheap venture. But it's what's going to separate organizations in the next five-to-ten years,” he said.
Two main data mechanisms are a foundation for a good AI program, according to Fornito. Machine learning is a structured data framework, and deep learning is for unstructured data.
When first starting the journey, an organization needs to have an AI champion promoting this process.
“One of the biggest gaps that I see within organizations is when there’s not a senior leader that is passionate about the value data and what it can provide. As much as I love AI, AI can't exist without the data. At the end of the day, it's math and science with some art mixed in, but it's the data that's really telling. So if you have an AI champion evangelizing and promoting that with the right team framework, the right hardware and software applications, and the right processes for that organization to be successful, then they're going to do so,” he said.
The first AI staff to hire depends on an organization's data. According to Fornito, if there is good data accessible, hiring a data scientist with an understanding of healthcare is best. It is essential to find someone who understands both the clinical and data science sides to succeed. According to Fornito, while understanding the data is necessary, what he cares the most about is making it actionable.
“If I can detect something with 99% accuracy, but that doesn't do anything to improve patient care, reduce costs, then it's basically a worthless model, right? There needs to be something that can be leveraged within the outcomes of that model for it to be relevant to the organization,” he said.
For a successful AI program in a healthcare organization, the business unit or data science team leader to understand use cases, associated costs, and IT. Without the involvement of the IT team, there is a concern for HIPAA compliance, deleted data, and data security, Fornito explained.
According to Fornito, it is a heavier task than people see, but he continues to look at what can be iterated and demonstrate value. He sees healthcare as a significant opportunity to improve the level of care and increase efficiency for patients and physicians.
“My goal or hope is really that leveraging AI in meaningful ways in the future is going to help drive us there. And I just love getting to talk in this space. It's really exciting. I love getting to talk with different customers because every single one has something unique that others want to do,” he said.
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