Powerful AI: Examining results of Waystar AltitudeAI™ in RCM

AI in healthcare revenue cycle management refers to the use of artificial intelligence — including generative AI, agentic AI, predictive analytics, and intelligent automation — to streamline the financial processes that span the entire revenue cycle. Applied across these workflows, AI in healthcare revenue cycle management helps reduce manual work, surface risk earlier, improve patient financial care, and accelerate reimbursement. Waystar AltitudeAI™ brings these capabilities together inside a single platform, with results that include a 70% time savings on appeal package creation, a 40% higher denial overturn rate, and more than $15 billion in denials prevented in under a year.¹

¹Waystar data.

Waystar AltitudeAI™ is a suite of AI-powered capabilities built into the Waystar platform to elevate productivity and accuracy across the healthcare revenue cycle. Built on Waystar’s robust data network, Waystar AltitudeAI™ anticipates challenges and proactively addresses inefficiencies to drive meaningful outcomes. It uses generative AI to autonomously draft appeal letters inside Waystar’s Denial + Appeal Management solution, automates repetitive revenue cycle tasks with an AI-powered assistant, and applies predictive analytics to forecast trends and surface potential denials before they happen. Recent results include a 70% time savings for appeal package creation and a 40% higher denial overturn rate.¹

¹Waystar data.

Generative AI does something traditional AI can’t — it creates new content instead of only analyzing what’s already there. In healthcare revenue cycle management, that opens up a range of use cases. Generative AI-powered software can:

• Draft appeal letters for denied claims
• Summarize clinical documentation and denial reasons
• Generate patient-facing billing explanations
• Power conversational assistants for staff and patients

Waystar AltitudeAI™ focuses generative AI RCM where it delivers the biggest time and revenue impact. For example, inside Waystar’s Denial + Appeal Management solution, it autonomously drafts appeal letters tailored to each denial. Trained on Waystar’s robust data network, it pulls from denial details, clinical documentation, and payer-specific requirements to produce ready-to-send letters in a fraction of the time. Creating 100 appeal packages used to take 38 hours manually. With AI-generated letters, it drops to 12 hours. 1 With Appeal + Reconsideration Blueprints, it drops to 2 hours.¹

¹Waystar data.

ROI from AI in RCM typically shows up in four places: faster appeal cycles, higher denial overturn rates, lower manual workload, and stronger revenue capture. Together, these translate into lower cost-to-collect, faster reimbursement, and more team capacity for complex work.

Waystar AltitudeAI™ has delivered appeal package creation 3 times faster, a 70% reduction in time per package, and a 40% higher denial overturn rate.1 Waystar AltitudeAI™–powered solutions have also helped prevent more than $15 billion in denials in under a year — concrete evidence of the financial impact AI can deliver at scale.¹

Before deploying any AI solution, organizations should tie the investment to specific outcomes — like denial overturn rate, days in AR, cost-to-collect, and denial prevention — so ROI can be tracked from day one.

¹Waystar data.

AI in healthcare RCM can be both highly secure and fully compliant when built on a strong foundation. Trusted vendors layer AI-specific safeguards, human oversight, and ongoing security testing — on top of established healthcare compliance frameworks.

Waystar AltitudeAI™ is built inside the same security and compliance program that protects the rest of the Waystar platform, which holds HITRUST CSF certification and SOC 2, PCI, and HIPAA compliance. Waystar AltitudeAI™ is in scope for Waystar’s 2026 HITRUST and SOC 2 + HIPAA assessments, and its agentic capabilities operate within defined guardrails and human oversight. When evaluating any AI vendor, providers should look at compliance certifications, data governance practices, AI-specific security testing, and how the vendor handles training data and PHI.