AI that **understands** healthcare

Explore how Cohere Health's AI supports clinicians, informs IT leaders, and improves outcomes for patients.

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Built for plans, trusted by clinicians.

Cohere Health’s clinical-grade AI isn’t just a language model—it’s a precision solution built for high-stakes clinical decision-making. It helps nurses, physicians, and UM teams make faster, more accurate decisions while preserving safety, oversight, and trust.

We take a minimum necessary approach. Instead of indiscriminately consuming EHR data, our AI focuses only on the essential clinical information needed to support better decisions to ensure effectiveness, responsibility, and trust in every interaction.

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Responsible AI
best practices

Reinforcement
model learning

Performance-driven retraining

Real-life
clinical feedback

30K daily transactions

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Feedback from
in-house PA experts

Why health plans trust Cohere Health

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Clinical-grade, precision AI

Trained on real clinical documentation, not generic text. Focused on only the data necessary to deliver safe, relevant, and policy-aligned decisions.

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Built-in oversight

Transparent, auditable, and always clinician-reviewed. No black boxes-just clear, explainable decisions.

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Tailored to your plan

Not a one-size-fits-all model. Designed around your unique guidelines and goals.

Why health plans trust Cohere Health vs. other AI vendors

Training data

Oversight

Decision quality

Patient-centric

Real-world proof

Enterprise-ready

Precision-trained on large-scale clinical documentation

Built-in clinician review & auditability

Policy-aligned, traceable & trusted evidence

Centered around clinical context and patient’s medical necessity needs

Proven in live UM workflows supporting over 660,000 providers daily

Fully integrated with payer systems; HITRUST, HIPAA, SOC 2 compliant; designed for auditability and scalability

Other AI vendors

General-purpose AI trained on web or claims data

Often lacks transparency or review

Outputs require manual QA

Designed for speed or automation, not optimized for patient context

Typically untested or piloted

Limited healthcare-grade features, compliance, or integration capabilities

Other AI vendors

Training data

Oversight

Decision quality

Patient-centric

Real-world proof

Enterprise-ready

Precision-trained on large-scale clinical documentation

Built-in clinician review & auditability

Policy-aligned, traceable & trusted evidence

Centered around clinical context and patient’s medical necessity needs

Proven in live UM workflows supporting over 660,000 providers daily

Fully integrated with payer systems; HITRUST, HIPAA, SOC 2 compliant; designed for auditability and scalability

Training data

Oversight

Decision quality

Patient-centric

Real-world proof

Enterprise-ready

Other AI vendors

General-purpose AI trained on web or claims data

Often lacks transparency or review

Outputs require manual QA

Designed for speed or automation, not optimized for patient context

Typically untested or piloted

Limited healthcare-grade features, compliance, or integration capabilities

AI Performance

Outperforms in the metrics that matter

Cohere’s AI models are developed in close partnership with clinicians, resulting from real-world observations of UM cases in over 40M+ clinical records. Our fine-tuned models consistently outperform state-of-the-art LLMs and are as accurate, if not more accurate than, expert nurse reviewers.

Detecting lab value ranges & trends

Extracting lab value information can be challenging for LLMs due to the complexities associated with tracking and extracting longitudinal values and their contextual relationships (e.g., units, reference ranges). Additionally, shorthand, abbreviations, and inconsistent terminology can be difficult for LLMs to interpret unless they are extensively trained on in-distribution medical text.

Illustration of collaborative agreement symbolizing delegated utilization management with clinical intelligence and peer-to-peer physician support
Bar chart showing performance comparison for conditions across Cohere Fine-Tuned Model, Llama 3.1-70b, Claude 3.5-180b, Deepseek Qwen 32b, and an Experienced Nurse, with accuracy percentages.

Representing the patient’s health status

Fine-tuning models enables us to capture the specifics of a patient’s presenting condition. Otherwise, LLMs struggle with important condition modifiers such as severity, related human anatomy, and the ambiguity & variability that are common in clinical notation.

Understanding nuanced diagnosis

Accurate interpretation of diagnosis details requires high precision context about temporal (e.g., onset, progression) and clinical modifiers (e.g., disease types and stages). LLMs often struggle to extract these nuanced relationships, given the non-standard language common in physician-narrated texts.

Illustration of collaborative agreement symbolizing delegated utilization management with clinical intelligence and peer-to-peer physician support
Bar chart showing performance comparison for procedures across Cohere Fine-Tuned Model, Llama 3.1-70b, Claude 3.5-180b, Deepseek Qwen 32b, and an Experienced Nurse, with accuracy percentages.

Verifying treatment performed

Treatments often span a broad scope (e.g., “conservative care”) that requires correlation to specific types (e.g., “physical therapy,” “rest”). LLMs often struggle with specificity when explicit ontologies or mappings are not available. Additionally, rich relational information is necessary to extract actionable procedure information.

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Beyond the Buzzwords

Download our whitepaper

Discover how AI and machine learning are addressing critical problems with the traditional prior authorization process in our latest white paper, How AI and Machine Learning are Transforming Prior Authorization Today.

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Nurses & MDs rating on AI-generated clinical content

Our AI features are trusted by experienced clinicians

Case review chatbot

An interactive chatbot to improve review accuracy and speed by surfacing relevant information from clinical and admin data (with citations)

Clinician rating our AI

Did the chatbot help you understand the clinical documents better?

90%

Was the chatbot answer correct?

79%

Was the chatbot answer complete?

60%

Would you trust the answer without verification?

47%

Want the strategy or the stack?

Clinical and operational leaders

Learn how our AI improves turnaround times, policy adherence, and provider satisfaction.

Technical and data leaders

Dive into our model architecture, data handling, and oversight safeguards.

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Explore our platform

See how our clinically trained AI streamlines health plan-provider collaboration

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