Healthcare technology company Xsolis is using artificial intelligence (AI) to address the evolving needs of its customers. In a recent interview, Xsolis Chief Medical Officer Heather Bassett discussed the company’s focus on AI and its efforts to support healthcare providers and payers.
Bassett, who has a background in internal medicine and a passion for problem-solving, has been with Xsolis since 2013. Over the past two years, Bassett has seen a shift in the needs of Xsolis’ customers. She noted that the healthcare industry is becoming increasingly complex, with a growing focus on administrative efficiency and cost reduction. In response, Xsolis is expanding its AI-driven solutions to support a wider range of operational challenges.
One of the company’s key areas of focus is the reduction of administrative waste. Bassett highlighted the significant amount of time and resources that are currently spent on manual workflows and inefficient processes. By leveraging AI, Xsolis is able to automate many of these tasks, freeing up time for clinicians and reducing costs for healthcare organizations.
Looking ahead, Bassett is optimistic about the potential for AI to drive meaningful change in the healthcare industry. She emphasized the importance of using AI responsibly and in a way that supports the needs of both patients and providers.
“I’m a pretty strong believer that there is a place for generative AI to be used responsibly within healthcare,” Bassett said. “And I’m going to emphasize the word responsibly.”
Watch the interview to learn more.
Heath Clendenning:
Welcome everyone, and thanks for joining today. I'm Heath Clendenning with Fierce Healthcare. Today we're speaking with Heather Bassett, who is the Chief Medical Officer of Xsolis. Heather, welcome.
Heather Bassett:
Oh, thank you, Heath. I'm excited to be here and have a great discussion today.
Heath Clendenning:
Yeah, we're really excited to have you on. So, tell us about your background and current role with Xsolis.
Heather Bassett:
So, I've had a love for math, science, computers, technology for as long as I can remember. I think part of it is that I just like to solve problems, and I was pretty fortunate to have parents that encouraged that. But after university I decided to kind of do a research pathway. I worked in DNA repair, which actually ties back to understanding and ultimately treating skin cancer, which I am very prone to actually and runs in my family. So, I really enjoyed doing that from a problem-solving standpoint. I then decided to go to medical school and ultimately chose a career in internal medicine. I worked as a hospitalist at a large facility here in Nashville, and in 2013 a door opened for me. I said yes and started working as the chief medical officer here at Xsolis, basically right from the beginning when we were still scratching ideas on a whiteboard. As you can imagine, I've worn a bunch of hats over the years.
I actually played a pretty key role in developing our core AI-driven analytic, the Care Level Score. That really was the beginning of our company saying there has to be a better way to support utilization review in mid-revenue cycle. We quickly recognized there was an opportunity to do that through purpose-built, AI-driven technology – and that was 10 years before ChatGPT created all of the hype. Our clients also saw our vision, because they were frustrated with a lot of manual workflows, inefficiencies, revenue leakage. We're now live, contracted with over 500 hospitals including major health systems across the US. I know this sounds a little corny, but it has been a privilege to walk hand in hand and really partner with our clients. I've worked with some great employees over the years through that journey. I'm pretty excited for what we plan to do in the near future.
Heath Clendenning:
That's awesome. Yeah, the history of your company in AI is really big. And actually I wanted to know – because Xsolis has been an AI in healthcare company solving these operational challenges for payers and providers since 2013 – how have customers’ needs evolved in the past few years? And, how is Xsolis responding with its solutions?
Heather Bassett:
The short answer is that our customers’ needs have become much more sophisticated and more complex over the years. I recently heard somebody say that healthcare in general is more complex than putting a man on the moon. I think a lot of it has to do with a tremendous amount of administrative waste. That creates inefficiency. It's a big reason there's a high degree of burnout among clinicians. They've kind of lost that joy of practice, and that administrative waste is costly. There are billions if not trillions of dollars tied up in healthcare that can be contributed back to this administrative waste. Hospitals have increasing cost pressures and very narrow margins that they're dealing with. So, the ability to tackle that administrative waste and take money back is a real opportunity for them. We're also in the middle of a staffing crisis. And, in addition to that, the population that's older than 65 is growing at a pretty rapid rate.
So, not only is that population getting bigger – and they're our sickest population – we don't have the staff to take care of them. Another big problem is that there's a lot of friction between two key stakeholders, the provider and the payer. That friction creates a lot of work that doesn't need to happen. It gets away from the opportunity to do things together and move that needle forward. As a company, we've really challenged ourselves to find better ways to tackle those problems and to really think outside of the box. So, we're continually trying to improve our current offerings that support utilization review in mid-revenue cycle. We're about to roll out our Dragonfly Utilize upgrade, which supports both providers and payers. We're working hard to continue to expand into case management and support length-of-stay initiatives. We currently have a host of length-of-stay, predictive AI models that clients use directly in their electronic medical record.
In 2025, we're going to roll out a Dragonfly Navigate solution to help support that. We continue to build our payer and provider network, which I think is probably some of the most important work we do. We actually made it a little hard on ourselves, because that is a challenging space to sit neutrally between those two parties and try to figure out how to move the needle – because the needle has not moved over the years. We're also looking to create a stronger network through interoperability and roll out some generative AI offerings in 2025 to support our UR nurses and physician advisors. That's just a little teaser of what we're up to.
Heath Clendenning:
Well, that's a lot going on there. And you did mention generative AI, which is a hot topic recently. It began disrupting across all industries in late 2023. And the opinions on it have varied widely – “it's going to solve all of our problems,” where some are afraid “it's going to take my job.” So, what's your take on gen AI, and how should it be used in healthcare?
Heather Bassett:
So, I'm a pretty strong believer that there is a place for generative AI to be used responsibly within healthcare. And I'm going to emphasize the word responsibly. ChatGPT, as you know, made a splash in 2023. And, you're right; there were two camps at that point in time. There's kind of that rose-colored view, “It can solve all our problems in healthcare.” And then, on the opposite end of the spectrum, there is the gloom and doom view, “It's dangerous; it's going to take everyone's jobs.” But fast-forward to where we are today, Q4 of 2024. In my opinion, we're in a different space right now. I think I really saw that because I do try to attend different AI conferences and hear what is being said by our healthcare partners and other vendors. And there really has been a shift to more of that middle ground, I guess you could say.
I think a lot of it has to do with we have a better understanding of generative AI, what it can do, where the risks lie, how you can mitigate some of those risks. And more importantly, we're starting to recognize what problems in healthcare it can actually help solve for. Because, you have to start with the problem. You have to make sure you're using the correct tool. And, in this case, generative AI is just one of the tools in your toolbox. I really feel like we're starting to really understand what it can solve for.
If you think about generative AI, it does a really good job of extracting and summarizing clinical information. Clinicians spend a tremendous amount of time charting, extracting data, creating narratives, and that has ultimately moved them away from the patient. I really do see – whether it's through ambient listening or the type of generative AI offerings that we plan to roll out next year that support UR nurses – and really do feel like they're going to help the clinicians start to bring that joy back into practice.
It’s interesting. I think you've heard me say this before, but we're moving into the future in order to go back in time. So, we're using new technologies, generative AI, to give our clinicians time back for more complex tasks and actually get them back to spending time with the patient, which is how it really used to be and how it should be.
Heath Clendenning:
What should health systems or health plans prioritize to ensure the successful deployment of AI solutions?
Heather Bassett:
Yeah, that's a great question, because it's critical that health systems and health plans have a plan in place. Otherwise, even the greatest AI product since sliced bread will fall flat on its face. You can't underestimate the importance of good change management and leadership buy-in. I think there are four things that are pretty important to set that framework. I'm sure I could list off a whole host of others.
But, you really need to start by setting the framework for your institution to be successful. That really should include an AI committee. That committee can help you keep abreast of regulatory changes and help you align with responsible AI best practices. They can also help you vet AI-driven solutions or products – whether they come from a vendor, or whether it's something you're building internally using your own data science team. There's some great resources out there that can help you with that process, CHAI (Coalition for Health AI), that's driven by healthcare systems and some other healthcare vendors like the Mayo Clinic, Advent Health, Amazon. There's the Responsible AI Institute, RAISE out of Stanford, NIST – there's quite a few out there that can help support that.
I'd also recommend that you start working with the vendors that you already have a partnership with. I think that's important because there are so many new players in this space. It's a lot of money (to invest), and some of them are better vetted than others. But if you have a vendor you trust, that you work with, they deliver AI experiences, they have experience in that AI space, they're really positioned to partner with you and continue to solve problems and help with initiatives.
The other thing you need to consider is you have to be willing to invest resources, your own subject matter experts, your own employees that test things and give feedback. That requires a lot of time, and you have to be willing to invest it today to see benefits in the next couple of years.
I think the last one is, I think we make a lot of assumptions about people's AI literacy. So, I recommend you spend time through education to increase the AI literacy of your C-suite and other leaders – and probably even more important, your day-to-day, get-the-work-done employees. Because the more you empower them, it allows your employees to then align with your overall AI objectives instead of being fearful and not engaging, so that you ultimately gain the benefit and really move your healthcare (organization) through the strategy that you have laid out.
Heath Clendenning:
Those are some fantastic tips. I look forward to seeing what the future holds for us. So, Heather, thank you so much for sharing your insight with us and our audience today.
Heather Bassett:
Oh, thank you, Heath. It was great to chat with you, and I look forward to more conversations.