Mendel for Providers

Supercharge your clinical data workflows

Mendel helps physicians and care coordinators to chat with the structured and unstructured EMR data with an AI that mimics a physician’s understanding of precision medicine

AI could revolutionize how health systems use data

UI screen with a checkmark to represent prior-authorization.
Prior-Authorization
Chart icon in the process of being scanned/reviewed.
Chart Review
Patient data in a magnifying glass to represent "Clinical Trial Matching"
Clinical Trial Matching

How health systems benefit from Mendel

Trial Matching

Find patients from your system who match the inclusion and exclusion criteria of trials being recruited for.

Prior-Authorization

Analyze patient data and determine the medical necessity by considering the patient’s complete history and applicable guidelines.

Chart Review & Guideline Matching

Analyze patient data to see what they are indicated for and analyze guidelines adherence.
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Enhancing clinical workflows with Hypercube
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Trial Matching

Mendel's distinct low-code/no-code capabilities and comprehensive data consolidation empower effortless, precise cohort creation for everyone in the network. The comprehensiveness of the underlying Hypergraph aligning with the increasing modern precision medicine demands.

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Prior-authorization

Mendel's AI-powered prior-authorization solution leverages the Hypergraph to analyze patient data and determine the medical necessity of treatments, procedures, and medications. By considering the patient's complete history and applicable guidelines, Mendel streamlines the prior-authorization process, reducing administrative burden and ensuring patients receive timely access to appropriate care.

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100% Explainable

Mendel's Hypergraph is designed to provide answers you can trust in a clinical setting. By leveraging a combination of machine learning and expert-curated knowledge, Mendel ensures that the insights generated are accurate, contextually relevant, and grounded in scientific evidence.

Every answer is linked to the support evidence from the original patient record tied to the results so that you can verify all responses.

Treatment Guidelines

Mendel's Hypergraph incorporates the latest treatment guidelines and protocols, enabling healthcare providers to make evidence-based decisions. By cross-referencing patient data with these guidelines, Mendel can suggest personalized treatment plans, identify potential contraindications, and alert providers to any deviations from best practices, ultimately improving the quality and consistency of care.
Mendel enables healthcare providers to make evidence-based decisions
Real Results

Enhancing Clinical Precision & Efficiency with Human + AI Collaboration

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+30%
Increase in Speed of Chart Review
Chart-level accuracy increasing icon
95%
Chart-Level Accuracy in Biomarker Testing
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+24-50%
Increase in Number of Patients Accurately Identified as Potentially Eligible
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Pricing

Copilots Catered To You

Hypercube Cohort

Good For
Anyone with a terminal: Infrastructure, data engineering, bioinformatics, data science, etc.
Data Type
Structured or unstructured
Core Features
Enables semantic reasoning

Hypercube Charts

Good For
Anyone who doesn’t enjoy code: Sales and marketing, R&D, medical affairs
Data Type
Structured or unstructured
Core Features
No code querying with both semantic and temporal reasoning

Hypercube Analyst

Good For
Anyone with a terminal: Infrastructure, data engineering, bioinformatics, data science, etc.
Data Type
Structured or unstructured
Core Features
Enables semantic and temporal reasoning

Build Your Own

Good For
Anyone with a terminal: Infrastructure, data engineering, bioinformatics, data science, etc.
Data Type
LLM agnostic
Core Features
Enables semantic reasoning
Hypercube Cohort icon
Hypercube Cohort
Rapid Cohort Building. Across All Your Data. By Everyone.
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Hypercube Charts icon
Hypercube Charts
AI copilot for chart reviews and abstraction tasks.
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Hypercube Analyst icon
Hypercube Analyst
Swiss Army Knife for clinical data discovery and curiosity.
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Build Your Own Hypercube icon
Build Your Own
LLM agnostic platform. Unmatched clinical reasoning capabilities.
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Hypercube Redact
Everyone claims accuracy. Mendel delivers certified results.
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Case Studies and Publications

View Success Stories

Got a question?

What makes your search actually work?
Two things: We couple large language modeling with a proprietary hypergraph that represents the complexity of precision medicine, getting models closer to physician-level understanding of the data. Second, we solved computational problems to traverse hypergraphs at laser-fast speed and affordable cost, enabling the technology to work in real-life scenarios.
What is a Hypergraph vs. a Knowledge graph?
A hypergraph is a generalization of a graph, allowing edges to connect any number of vertices. Knowledge graphs are typically based on simple graphs, limiting the representation of complex relationships.
Why can’t I build my own Hypergraph?
Building a clinical Hypergraph requires extensive domain expertise, large-scale data curation, and complex algorithmic development. It is a time-consuming and resource-intensive process that most organizations may find challenging to undertake independently.
Are Hypergraphs slow?
Not necessarily. Our Hypergraph technology utilizes innovative algorithms and data structures specifically designed for efficient hypergraph analysis, enabling quick traversal and querying even with a large number of nodes and relationships.
Who are the experts behind the Hypergraph?
Our Hypergraph is developed by a team of AI scientists and clinical experts who have worked together for years to curate knowledge and build a scalable solution tailored to the unique challenges of the clinical domain.
What ontology are you using?
We have developed a proprietary ontology called the “Generative Ontology” that combines principles of generative grammar and ontology to create a scalable and adaptable system for modeling the clinical domain.
Can I trust the logic?
Yes, our Hypergraph is expert-curated and open, allowing clinical teams to audit the logic. The underlying language models are trained on actual medical data and records, not just internet data.
Is this a rule-based system?
No, our platform combines both symbolic reasoning (through the Hypergraph) and machine learning (through language models) to achieve a more comprehensive and flexible approach to clinical reasoning, going beyond traditional rule-based systems.

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