An applied AI lab innovating healthcare revenue solutions at scale

Keeping up with payer policy changes requires the monitoring of tens of thousands of publications that affect whether claims will be accepted or denied. Our team is building agents that apply AI to monitor over 30,000 ongoing policy changes and over 20,000 proprietary billing rules to turn them into applied intelligence so that accuracy is guaranteed.

Medical coding is a critical step in translating clinical language into the billing language that payers require. It’s an area where mistakes are costly. The R37 team is applying cutting-edge AI to autonomously code encounters combined with the diligence of our DRG-V safety net, industry expertise and depth of data & regulatory learnings. By bringing these areas together the team is moving towards full automation of the mid cycle.

Improving net revenue begins by identifying the best possible payment for every service. Doing this at scale historically has required tedious contract analysis and financial modeling, but by leveraging cutting edge Large Language Models we are radically simplifying this process. This research area is helping R1 clients increase additional net revenue and capture more underpayments.

Clinical denials occur for a broad set of denial reasons and are a constant, costly challenge for providers. Our team is exploring the best ways to apply AI to automate the end-to-end workflow from denial receipt to appeal submission, as well as experiment with strategies that maximize overturn rates.

Hospital call centers interact with a high volume of callers daily. From appointment scheduling to insurance verification and denial follow up – each call represents an opportunity for greater efficiency. This research agenda is focused on A/B testing, refining voice flows for inbound and outbound calling, and performance benchmarking across millions of annual interactions.

Reasoning models are at the heart of RCM. Beyond pattern recognition, AI can make logical inferences about documentation, claims and reimbursement pathways. Within our lab, we’re exploring how these advanced reasoning models can drive smarter automation and decision support across the revenue cycle.