Healthcare decisions are only as effective as the information that informs them. Yet, in the current U.S. system, members’ clinical histories are dispersed across multiple providers, facilities, and systems, often in formats that are difficult or slow to consolidate. This fragmentation limits visibility, reduces the efficiency of care, and creates gaps that cascade into higher costs, preventable complications, and operational inefficiencies for health plans. Addressing these gaps is critical to enabling care that is both personalized and scalable.
In 2024, 10% of patients reported having to repeat a test or procedure because earlier results were not available, highlighting the real cost of incomplete records and insufficient context in care decision‑making.
Achieving a complete, longitudinal view of a patient is critical during care delivery to reduce unnecessary utilization that impacts a payer’s network efficiency.
Retrieval‑Augmented Generation (RAG) represents a next‑generation AI architecture that prioritizes context before generation. Unlike general-purpose LLMs that synthesize responses primarily from patterns in training data, RAG frameworks retrieve the most relevant sources of truth, such as a member’s history, prior treatments, lab results, latest medical evidence, public health data (like Flu trends), and benefit coverage, before proposing actionable guidance.
Counsel embeds this RAG AI framework into care delivery, orchestrated by a context-retrieval agent part of a broader proprietary agentic framework. This RAG pipeline dynamically pulls context that Counsel AI utilizes throughout every interaction.
This context-retrieval agent gathers clinical data from disparate sources, including medical history from Health Information Exchanges (HIEs) and EHRs, clinically validated external sources, and prior interactions with Counsel. The relevant context informs the medical advice provided by Counsel AI and equips physicians with what they need to provide additional care, design treatment plans, prescribe medications, and more.
Because it retrieves context first and then synthesizes it with medical reasoning, Counsel’s RAG AI is designed to reduce generic or incomplete outputs, effectively informing a series of agents that perform tasks, such as detecting emergencies and determining doctor escalations. Multiple, independent safeguard agents run in every interaction, ensuring every exchange meets clinical, legal, and payer-level standards for safety and compliance.
Counsel is built to complement, not replace, a health plan’s existing care infrastructure. By ingesting its ecosystem and networks, the platform can optimize escalation protocols where clinically appropriate. Use cases include:
By connecting clinical reasoning with a payer’s existing ecosystem and provider networks, Counsel’s RAG AI framework turns context into actionable care pathways,
Partnering with an AI-enabled primary care solution that leverages a clinically-validated and optimized RAG AI framework delivers measurable strategic value:
Request a demo and discover how Counsel’s AI-enabled model leverages its agentic framework and RAG pipeline to deliver care that is secure, personalized, and continuous.
The Counsel Health editorial team is a multidisciplinary group of writers and editors dedicated to delivering clinically grounded, evidence-based health information. Their work is informed by real-world care delivery and guided by physician expertise, ensuring content is accurate, accessible, and trustworthy. By translating complex medical topics into clear, practical guidance, the team helps readers understand their health, explore care options, and make informed decisions in a rapidly evolving healthcare landscape.
Dr. Cían Hughes is a physician-scientist with over a decade of experience in health AI research. He began his career as an academic surgeon and, in 2015, joined Google DeepMind as its first Clinical Research Scientist, helping to found the DeepMind Health team. Prior to DeepMind, he was an NIHR Academic Clinical Fellow in Otolaryngology at University College London, working across the UCL Ear Institute and the Farr Institute while maintaining clinical practice.
Our content is created for informational purposes and should not replace professional medical care. For personalized guidance, talk to a licensed physician. Learn more about our editorial standards and review process.
