Scaling Agentic AI in Healthcare: From Pilot Projects to Production Impact

Learning from past AI cycles

Over two decades of building advanced AI systems, from academic labs to enterprise deployments, the field has seen cycles of hype and correction. The earlier 'AI Winter' showed how expert systems attracted massive investment yet often failed to deliver in complex domains. Today's large language models (LLMs) represent a major technical leap, but treating them as a prompt-driven silver bullet risks repeating past mistakes: prompt-based adoption can behave like a rule system wrapped in natural language and produce unreliable outputs when deployed without deeper grounding.

Grounding LLMs with neuro-symbolic methods

At Ensemble, we focus on the next step beyond prompt engineering: grounding LLMs in facts and logic through neuro-symbolic AI. This hybrid approach pairs the intuitive and generative strengths of LLMs with symbolic representations and deterministic reasoning. In regulated and safety-critical fields like health care, relying on purely statistical language models is insufficient. Symbolic structures capture taxonomies, clinical rules, and guidelines that must be adhered to for compliance and accuracy.

By integrating LLMs and reinforcement learning with structured knowledge bases and clinical logic, a neuro-symbolic architecture reduces hallucinations, extends systematic reasoning, and ensures decisions are anchored to verifiable evidence and enforceable guardrails.

Three pillars of a scalable agentic AI strategy

Ensemble's agentic AI approach rests on three complementary pillars:

1. High-fidelity datasets

Access to rich, longitudinal clinical and administrative data is essential. Ensemble has harmonized more than 2 petabytes of claims data, 80,000 denial audit letters, and 80 million annual transactions mapped to industry outcomes. This dataset underpins EIQ, an end-to-end intelligence engine that models the 600-plus steps of revenue operations and supplies structured, context-rich inputs to agentic systems.

2. Collaborative domain expertise

AI scientists work directly with revenue cycle domain experts, clinical ontologists, and data labeling teams to design use cases that reflect regulatory constraints and payer-specific logic. Embedding end users in development and post-deployment feedback loops catches friction early and drives rapid iteration. This trilateral collaboration—AI researchers, health-care experts, and front-line users—yields systems that mirror experienced decision making while preserving human oversight.

3. Elite AI scientists and resources

Ensemble's incubator attracts AI talent from leading institutions and industry. Researchers with PhD and MS credentials and backgrounds at FAANG companies conduct cutting-edge work in LLMs, reinforcement learning, and neuro-symbolic AI within a mission-driven setting. They also have access to sensitive, high-quality health-care data and infrastructure that enables large-scale experiments and real-world impact.

Real-world applications and early results

Ensemble has moved beyond pilots into production and larger-scale pilots across hundreds of health systems:

Supporting clinical reasoning

Neuro-symbolic systems rewrite clinical guidelines into a proprietary symbolic language that humans verify. When a hospital faces a denial, an LLM parses the patient record into that symbolic language, matches it deterministically against the guidelines to find justifications and evidence, and then generates a denial appeal letter grounded in the record. Early deployments improved denial overturn rates by 15% or more for Ensemble clients. Similar capabilities are being piloted for utilization management and documentation improvement to reduce denial and downgrade risks.

Accelerating accurate reimbursement

A multi-agent reasoning model coordinates autonomous agents to interpret account details, retrieve required data, decide next actions, automate routine resolutions, and escalate complex cases to humans. This orchestration aims to reduce payment delays and administrative burden, improving financial outcomes for hospitals and patients.

Improving patient engagement

Conversational AI agents handle inbound patient calls naturally and route to human operators when needed. Operator assistant agents provide transcriptions, surface relevant data, suggest next-best actions, and streamline follow-up. Ensemble reports a 35% reduction in patient call duration, higher one-call resolution rates, and roughly 15% improvement in patient satisfaction metrics.

Building agentic AI that scales

Scaling agentic AI in health care requires rigor: the technology must be grounded in clinical logic, informed by domain experts, and built on exceptional data and research capabilities. By combining neuro-symbolic techniques with elite AI research and domain collaboration, Ensemble demonstrates a path from pilot experiments to meaningful, scalable impact that improves provider workflows and patient experiences.