Hospitals are working on very thin margins, and every dollar matters to them. Administrative costs now account for more than 40% of U.S. hospital expenses, with over $160 billion spent annually on RCM. As providers face escalating economic pressures and complex payer relationships, AI-native solutions have become a necessity. Healthcare providers need a fundamentally new approach to optimizing revenue so they can focus on patient care.
The Importance of AI in the Revenue Cycle
The right AI technology can help support improving clinical documentation, charge capture, DRG downgrades and more. It can speed up the time to file a denial appeal from 60 minutes to just 20, allowing clinicians to triple their productivity in just that one area.
Unfortunately, most providers simply don’t have the resources to do that on their own. That’s where partnerships can help bridge the gap, optimizing revenue recovery and giving providers the revenue they need to serve their communities.
What can AI do that previous technology can’t?
Stefan Fadel, senior vice president of patient entry operations at R1, explained how AI differs from previous technology used in revenue cycle management, including robotic process automation and electronic medical records.
“If you look at the landscape of healthcare and all the different technology shifts we’ve been a part of as a healthcare ecosystem, the landscape that we’re in is very complex,” Fadel said. “We’ve tried automation, RPA, machine learning, and other initiatives to really reduce the friction that we see in the revenue cycle. I think AI is the perfect enabling capability for us to take it to the next level and just reduce that friction out of the process, so the revenue cycle runs more efficiently. Our patients feel it, and most importantly, our customers feel it.”
The R37 lab is building the future of RCM
Mark Sithi is the senior vice president of product at R1 and leader of the product management team for R37, created in partnership with Palantir.
He explained the goals of the R37 lab and how it will transform the revenue cycle for healthcare providers. Delivering AI-driven solutions that will outperform anything currently in the market, the R37 lab will provide tools for healthcare providers that will perform faster and more efficiently than ever.
“We are merging R1’s technology team with Palantir’s AI platform with their forward-deployed engineers,” he said. “They have a great, AI-enabled platform that allows us to go quickly. That combines with R1’s deep subject matter expertise. We’ve embedded operations into the software development lifecycle with our engineers, our product team, and with Palantir.”
“We bring users into the room with the subject matter experts.” Sithi said. “We’re dissecting the problems, taking various use cases across the revenue cycle and iterating quickly so that we can use AI as effectively as possible.”
How is this approach different from what other companies are doing?
R1 brings decades of RCM expertise and Palantir brings the best AI-native platform that can deliver solutions at unprecedented speed. The partnership is about leveraging combined capabilities to redefine what’s possible in RCM. “We can be more holistic around AI innovation because R1 already serves the end-to-end process. We can understand how to apply AI throughout the revenue cycle not just at one point,” Sithi said. “We are creating autonomous AI agents that can, for example, overturn authorization denials on the back end. The intelligence that’s powering that authorization on the back end certainly can be used on the front end. But AI agents are only as good as the data that they are given.”
R1 has advantages that other RCM providers and partners do not. Every day, R1’s team of more than 34,000 RCM experts and global employees work with 94 of the top 100 health systems and more than 3,700 hospitals across the U.S.
What are AI agents?
Agentic AI is the next leap forward from the generative AI that most people are familiar with from tools like Chat GPT, Google Gemini and Microsoft Co-pilot. Generative AI generates answers to queries from information and patterns that it has already learned. Agentic AI creates agents that make autonomous decisions and take actions to achieve specific goals, adapting to new information and environments, often without human intervention.
Fadel explains that agentic AI is “a digital workforce capable of learning and performing tasks like payer outreach for health systems.” AI enhances interactions with payers by optimizing the process of request and submission. Agentic AI can perform tasks such as analyzing payer requirements and preferences, then automatically routing requests to the most appropriate channel (API, portal, RPA or phone). It can also automate the execution of payer preferences based on previous behavior, analyze payer patterns and predict the likelihood of capturing full payment.
It’s important for healthcare organizations to embrace AI, both generative and agentic. Health systems that delay adoption will struggle against more agile competitors who embrace AI to lower costs and improve margins. The R37 lab was created to help healthcare providers overcome these hurdles and recover every dollar they’ve earned.
Ready to learn more about what’s next for AI and RCM? Contact us!
