December 19, 2023

Using GenAI to Address Real Customer Needs

December 19, 2023

AWS points to Preverity as a model for using GenAI to address real customer needs

In today’s blog, I share why AWS called out Preverity as a leader in moving GenAI from hype to relevance through the customer use case.

At Preverity, we’re always pushing the boundary for healthcare analytics, and sometimes, we forget to celebrate the successes we’re having with our partners. So, as we approach year-end and enjoy a moment of reflection, I’m proud to share that AWS (Amazon Web Services) recognized our progress using generative artificial intelligence (GenAI) at the annual AWS re:Invent conference. AWS channel leader Ruba Borno cited Preverity as a model for effectively using generative AI to help healthcare and technology companies manage, analyze and model healthcare data.

Why did AWS call out Preverity as a model for generative AI? Because we focus our efforts on solving real-world customer problems. The one thing that will make GenAI “move from hype and interesting right now to fascinating and relevant is the customer use case.” Borno went on to describe why our focus on defining and solving customer problems leads to Preverity’s success with GenAI:

Preverity, which helps healthcare and technology companies manage, analyze and model healthcare data, optimized how it [AI] aggregated data for risk assessments.
What used to take their analysts weeks to code and develop measures and tie to clinical risk now can be done very, very quickly. When evaluated against over one million physician years of claim event activity, these results yield actionable results. 
“They’ve predicted savings in hundreds of thousands of dollars just in the first year…they’re able to do it faster, reducing costs, speeding up the outcome to the customer. That’s one use case for a specific industry and a specific type of customer,” said Borno. “That’s what we’re excited about with our partners, thinking through the top use cases by industry that we can work on together.”

I couldn’t have said it better. Like all advanced analytics, our approach to AI is to start with a customer need, identify a problem to solve, and iterate until we nail it. While we share the excitement over the power of GenAI, we start by spending time with clients defining the problems they haven’t been able to solve. 

Here are just a few examples where we ask questions to define the underlying problems and develop customer use cases:

    • New Physician Evaluation | How do I assess physicians who meet my hiring criteria? How do I pre-screen candidates to be sure they fit my desired risk profile? 
    • Ongoing Compliance | Do the activities of the physician align with industry-defined clinical guidelines? Are active physicians appropriately credentialed?
    • Risk Analysis | Where is the risk in my provider portfolio? How does it compare to other facilities and specialties in this state?
 

Developing a successful customer use case for healthcare risk analysis

Let’s dig deeper into risk analysis to show how we develop and solve for a customer use case. Detecting clinical risk before an event happens directly improves patient safety and helps avoid potential malpractice exposure. A case in point is detecting risk in the delivery room. Deliveries with complications can lead to tragic outcomes and are a leading cause of malpractice litigation.

One potentially risky procedure is vaginal birth after cesarean (VBAC), which should only be performed by obstetricians and then only under certain conditions. One of our successful customer use cases has been determining VBACs performed by individual providers, assuring that the hospital has the appropriate infrastructure in place and that an individual’s VBAC rate is consistent with standard rates in the region. Sounds simple, but this insight is a powerful tool for a hospital risk manager talking to an individual physician.

Our stats show that Preverity’s patient safety monitoring system utilizes clinical data from 80% of the US market—more than 80 billion patient interactions. But the power of that data is only realized when we apply advanced analytics, including generative AI, to a real-life issue defined as a customer use case. We moved quickly through a partnership with one of AWS’s first Generative AI Partners, Innovative Solutions. By integrating this AI solution into our data analysis processes, we already use GenAI to solve client problems faster.

Perhaps you were one of the half-million people who attended AWS re:Invent online or the 27,000 who attended in Las Vegas last month. If not, check out Preverity News for related articles. We’re spreading this AWS success story to challenge healthcare systems and insurers to identify issues that can be tackled with rich data and AI solutions. If you’re interested, contact me to learn how we use powerful analytics to help you solve real-life problems.

Kind Regards,
Gene Boerger, President and Chief Operating Officer

615-982-7076 | info@preverity.com