Johns Hopkins University School of Medicine received a $10 million donation for an endowed AI center to further research and innovation in clinical and non-clinical spaces. There is rapid evolution of clinical AI, but who will pay for the development?
T.Y. Alvin Liu, MD, the director of the James P. Gills Jr., M.D., and Heather Gills Artificial Intelligence Innovation Center, sees the legacy technology reimbursement system in the U.S. as a potential limiting factor.
“Artificial intelligence is currently the biggest opportunity and will remain the leading force for change in the foreseeable future,” he said during an interview with the “Becker’s Healthcare Podcast.” “However, despite the generally rosy sentiments, significant headwinds do exist for real world scaling of these clinical AI tools. If you look at a recently published report by the FDA, there are now over 1,000 AI-enabled medical devices that have been approved by the FDA. Yet, if you look at real world deployment of these tools, we are still in the infancy stage. The key uncertainty is how we are going to pay for these clinical AI tools.”
The FDA approval process is costly for companies, and they need to turn a profit. Typically, companies pay for research and development through CPT coding, but since AI is so new, there isn’t an applicable CPT code for reimbursement. Companies have the option then to apply for a CPT code through the American Medical Association.
“Let’s say you go through the whole process and manage to get a new CPT code to pay for your AI tool. Then you have to go to the insurance payers and convince them to actually pay for it,” said Dr. Liu. “Last but not least, even if you get some commitment from the payers to pay for the AI tool, there’s no guarantee that it will be reimbursed at a financially viable level.”
There has been much more innovation in AI tools related to administrative tasks, such as revenue cycle management, for a variety of reasons:
- There is less risk when a mistake is made;
- Administrative applications don’t require FDA approval;
- It’s easier to demonstrate the return on investment.
There is enormous pressure for health systems, and particularly academic systems, to become more efficient and invest smartly in AI. Academic systems often lead the charge in innovation because they have resources, scale and education mission. But they can also get stuck in bureaucracy.
“We have to critically evaluate our role in the rapidly evolving AI landscape and strategically position ourselves to maximize where we can add value,” said Dr. Liu. “One thing to keep in mind is the fact that a majority of care in the U.S. is delivered by integrated health systems and we will always be the ones performing the last mile of delivery when it comes to patient care. It is critical for us to strengthen our collaborations with startups, industry and venture capital investment funds to ensure that we have a voice in how these AI healthcare products are developed and fine tuned from day one. We should also invest in implementation science and change management to transform ourselves into world experts and real world implementation of AI tools in order to maximize their positive impacts on patient care.”
