Hospital for Special Surgery, the New York-based orthopedic academic medical center, now processes 1,100 insurance claims a month with AI agents and has cut appeals handling from 45 minutes to five, with the success rate climbing from 65% to 100% in the nine months since rollout. The agents, built in partnership with enterprise developer Ema Unlimited, have let HSS pull claims work back in-house after years of relying on a third-party contractor to handle the volume. It is one of the most concrete operational results yet published for agentic AI in a US hospital.
The stakes for the sector are large. The World Health Organization projects a global shortfall of 11 million health workers by 2030, and KPMG says 68% of providers have already deployed AI agents in some form. A separate KPMG figure puts 84% of providers as comfortable handing specific decisions to those agents, a sharp reversal from the cautious posture the industry held when electronic health records first rolled out in the early 2000s.
Ashis Barad, chief digital and technology officer at HSS, frames agentic AI as a different category from telehealth or remote monitoring, both of which improved access but left the administrative burden largely intact. AI agents, he argues, can handle nuanced cases without defaulting to a human escalation, retrieve information from clinical sources, and iterate over time. That is the gap previous waves of health-care digitization never closed.
“Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.”— Ashis Barad, Chief Digital and Technology Officer, Hospital for Special Surgery
Key facts
- 01AI agents at Hospital for Special Surgery now complete 1,100 insurance claims per month, work previously split between HSS staff and a third-party contractor.
- 02Appeals time dropped from 45 minutes to 5 minutes, and the success rate climbed from 65% to 100% in the nine months since deployment.
- 0368% of health-care providers have already adopted AI agents, per KPMG, with 84% comfortable delegating specific decisions to them.
- 04The WHO projects a global shortfall of 11 million health workers by 2030.
- 05HSS partnered with Ema Unlimited on a 24/7 AI scheduling and triage service accessible via web, text, or phone.
Building on the claims work, HSS is now pushing AI agents into patient-facing roles. A 24/7 scheduling and triage service, accessible by web, text, or phone, uses conversational AI to ask clarifying questions about a patient's condition and then books appointments based on the clinician's specialty, location, insurance coverage, and availability. The agent is trained on HSS's internal protocols, policies, and care pathways.
Safeguards sit on top. Sensitive, complex, or uncertain scenarios are escalated to human specialists, every agent decision is auditable, and human staff can intervene at any point. Decisions about which workflows to automate are filtered through an internal AI subcommittee that Barad co-chairs with a senior nursing executive, with patient-facing agents reviewed far more rigorously than back-office ones.
Barad's framing is that piecemeal use cases miss the point. He plans to open a dedicated AI lab at the HSS Manhattan campus offering classes and one-on-one training so any staff member can build or deploy agents — an approach Deloitte research associates with the more successful agentic adopters in health care, who tend to redesign end-to-end workflows rather than bolt agents onto narrow tasks.
“It's wrong to think of agentic AI in use cases… It's a general-purpose technology, analogous to electricity.”— Ashis Barad, Chief Digital and Technology Officer, Hospital for Special Surgery
The data foundation is where most providers stall. US patient records migrated to electronic systems two decades ago but remain fragmented across departments, providers, and legacy IT stacks, with even basic operational metrics defined differently from one hospital to the next. Barad notes that something as routine as "time to start surgery" has had a different definition at every hospital he has worked at, a fragmentation that blocks agents from assembling the tacit, cross-source context that distinguishes them from rule-based automation.
HSS's ambition is to push 90% of non-clinical tasks onto AI agents, freeing clinicians for what Barad calls white-glove work — the most specialized and sensitive cases. That number is aspirational rather than a current state, and the harder questions sit in patient-facing deployments: liability when a triage agent miscategorizes a symptom, payer acceptance of AI-drafted appeals, and the audit trail regulators will demand once decisions touch clinical care directly. None of those are resolved by the current rollout, and a 100% appeals success rate over nine months is a small sample against the variability of US payer behavior.
Still, the HSS numbers are unusually specific in a market where most agentic-AI claims are stated in capability rather than throughput. Claims processing, prior authorization, and appeals are exactly the high-volume, rule-heavy, well-documented workflows where agents should win first, and the cost arithmetic is straightforward — every minute saved on a 45-minute appeal is repriced staff time. Ema Unlimited, by anchoring an early customer with auditable results, gets a reference deployment far stronger than the typical pilot.
The broader signal is that health care's AI adoption is shifting from chatbots and ambient scribes — the categories that defined 2024 and 2025 — to revenue-cycle and triage agents that touch the parts of provider P&Ls that actually move. If KPMG's 68% adoption figure holds, the competitive question for vendors is no longer whether hospitals will buy agentic AI, but whose stack handles the data-integration problem well enough to make the agents perform outside a single flagship customer.
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