AI that understands healthcare
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—ensuring effectiveness, responsibility, and trust in every interaction.
Why health plans trust Cohere Health
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.
Built-in oversight
Transparent, auditable, and always clinician-reviewed. No black boxes — just clear, explainable decisions.
Tailored to your plan
Not a one-size-fits-all model. Designed around your unique guidelines and goals.
| Cohere AI | Other AI vendors | |
|---|---|---|
| Training data | Precision-trained on large-scale clinical documentations | General-purpose AI trained on web or claims data |
| Oversight | Built-in clinician review & auditability | Often lacks transparency or review |
| Decision quality | Policy-aligned, traceable & trusted evidence | Outputs require manual QA |
| Patient-Centric | Centered around clinical context and patient’s medical necessity needs | Designed for speed or automation, not optimized for patient context |
| Real-world proof | Proven in live UM workflows supporting over 600,000 number of providers daily | Typically untested or piloted |
| Enterprise-ready | Fully integrated with payer systems; HITRUST, HIPAA, SOC 2 compliant; designed for auditability and scalability | Limited healthcare-grade features, compliance, or integration capabilities |
| Cohere AI | Other AI vendors |
|---|---|
| Training data | |
| Precision-trained on large-scale clinical documentations |
General-purpose AI trained on web or claims data |
| Oversight | |
| Built-in clinician review & auditability |
Often lacks transparency or review |
| Decision quality | |
| Policy-aligned, traceable & trusted evidence |
Outputs require manual QA |
Clinicians trust it. The numbers prove it.
Outperforms in the metrics that matter
AI Performance
Cohere’s AI models are developed in close partnership with clinicians, resulted from real-world observations of UM cases in the 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.
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.
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.
Nurses & MDs rating on AI-generated clinical content.
Our AI features are trusted by experienced clinicians
| Review note generation Auto-generation of clinical case summary that reviewers typically have to author manually |
Clinician rating our AI |
|---|---|
| Do AI-generated notes save time? | 87% |
| Did you have to delete irrelevant info from the generated note? | 29% |
| Did you have to delete incorrect info from the generated note? | 4% |
| Review note generation Auto-generation of clinical case summary that reviewers typically have to author manually |
Clinician rating our AI |
|---|---|
| Do AI-generated notes save time? |
87% |
| Did you have to delete irrelevant info from the generated note? |
29% |
| Did you have to delete incorrect info from the generated note? |
4% |
| Case review chatbot
An interactive chatbot to improve review accuracy and speed by surfacing relevant information |
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% |
| 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% |
