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What is Valar Labs?

The landscape of cancer treatment is riddled with choices that often come with significant uncertainty, leaving many patients without adequate support from available resources. This gap not only leads to unnecessary fatalities but also results in the waste of billions within the healthcare system. Our dedicated team, comprising specialists in both computer science and oncology, is committed to confronting these pressing challenges in cancer treatment through the application of artificial intelligence. We are in the process of creating advanced deep learning models designed to analyze individual patient data, providing crucial support to oncologists as they navigate their decision-making processes. By prioritizing oncologists in the development of our AI tools, we guarantee that the insights produced are both interpretable and applicable in real-world scenarios. In partnership with top academic medical centers and practicing oncologists, we are working to address pivotal clinical questions that can lead to better patient outcomes. Our ultimate aim is to refine the decision-making process in cancer care, thereby alleviating the burdens faced by patients and healthcare providers alike, and paving the way for a more effective treatment paradigm. This initiative stands to usher in a new era of personalized cancer therapy, where decisions are informed by data-driven insights tailored to each patient's unique circumstances.

What is GenHealth.ai?

GenHealth.ai is an innovative generative AI platform specifically crafted for the healthcare industry, leveraging a proprietary Large Medical Model (LMM) that has been built from the analysis of over 100 million patient records rather than relying on traditional natural language data. This advanced LMM is proficient at deciphering medical codes and events, which enables it to predict patient outcomes, calculate costs, and simulate clinical pathways with far greater precision than conventional large language models. The platform offers a variety of customized applications, including Intake Automation for effective management of PDF routing and data extraction, a Prior Authorization Agent that streamlines the approval process, and G‑Mode analytics that empowers users to explore both historical and predictive population health information using natural language, all without any need for coding skills. Impressively, this AI co-pilot has demonstrated a remarkable 94% accuracy in prior authorization cases, achieved a staggering 120-fold increase in forecasting medical loss ratios, and improved cost predictions by 110% in comparison to traditional Hierarchical Condition Category (HCC) scoring methods. Furthermore, GenHealth.ai is set to revolutionize the healthcare sector by enabling more efficient, data-driven decision-making and enhancing overall patient care outcomes through its advanced technological capabilities.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Valar Labs

Date Founded

2021

Company Location

United States

Company Website

www.valarlabs.com

Company Facts

Organization Name

GenHealth.ai

Company Location

United States

Company Website

genhealth.ai/

Categories and Features

Categories and Features

Population Health Management (PHM)

Analytics
Cost-of-Care Analysis
Data Storage
EMR/EHR Integration
Patient Engagement
Patient Identification
Patient Risk Stratification
Patient-Reported Outcomes
Payment Bundling
Predictive Alerts
Test & Treatment Reminders
Utilization Tracking

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