Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health, reports that AI agents now complete 1,100 insurance claims per month. According to Ashis Barad, MD, its chief digital and technology officer, the agents have reduced the appeals stage from 45 minutes to five and improved the success rate of those appeals from 65% to 100% in the nine months since implementation, and HSS now handles all claims in-house.
Barad describes the shift this way: “Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.”
The claims work previously took several weeks to complete and involved both HSS staff and a third-party contractor to handle the volume. Barad says telehealth services and digital care tools such as remote monitors helped improve access to health care by removing geographical barriers, but failed to replicate the quality of in-person care or win trust from patients.
Building on the claims work, HSS is now deploying AI agents in a non-clinical, patient-facing AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then books appointments with the most appropriate clinician, factoring in location, insurance coverage, and physician availability. The agent is trained on HSS protocols, policies, and care pathways. Sensitive, complex, or uncertain scenarios are escalated to human specialists, every decision made by the agent is auditable, and human staff can step in at any point.
All decisions around the technology are filtered through an AI subcommittee that Barad co-chairs alongside a senior nursing executive, and agents that may touch on patient care are scrutinized with far more rigor than backend processes. Barad notes that each hospital he has worked in has had a slightly different definition for “time to start surgery,” and says this fragmentation impedes AI agents from retrieving information from different sources. With greater data interoperability at HSS, patient-facing agents can draw on a patient's clinical care history and existing recommendations from their clinician, combine them with current symptoms, and decide whether escalation is required before notifying the correct specialist and informing the patient.
Barad has plans to create a dedicated AI lab at the HSS main campus in New York City, open to all staff looking to understand or build AI agents, with classes and one-on-one training. He envisions a future in which 90% of non-clinical health-care tasks could be administered by AI agents.
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