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What is Hathr AI?

Hathr AI offers HIPAA-compliant AI chat solutions, API access, and enterprise-level tools, all powered by Anthropic's Claude, allowing healthcare providers, insurers, and professionals managing HIPAA-regulated information to enhance their workflows while ensuring data protection remains a top priority. Designed within the secure confines of AWS GovCloud’s FedRAMP High environment, Hathr AI guarantees that all data exchanges are kept confidential and shielded from unauthorized access. Users can streamline essential tasks such as summarizing patient notes, drafting pre-authorizations, and submitting insurance claims, all through a secure and user-friendly interface. Utilizing sophisticated models like Claude 3.5 Sonnet, Hathr AI creates a private AI setting specifically designed for compliance with HIPAA regulations. This enables teams to effectively extract and condense information from intricate medical records, which in turn aids in making better-informed clinical and administrative choices. With its advanced capabilities, Hathr AI not only enhances operational efficiency but also fosters a more secure environment for sensitive health data management.

What is Amazon Comprehend Medical?

Amazon Comprehend Medical is an NLP service designed to adhere to HIPAA standards, employing machine learning to extract health information from medical documents without necessitating any prior expertise in machine learning from its users. A vast amount of healthcare data is found in unstructured formats, such as physicians' notes, clinical trial reports, and patient histories. Relying on traditional, manual methods for data extraction is not only time-consuming but also prone to errors, as rule-based automation often fails to capture essential contextual details, resulting in incomplete data retrieval. This lack of reliability can significantly undermine the effectiveness of large-scale analytics, which are critical for advancements in the healthcare and life sciences industries, ultimately impeding potential enhancements in patient care and operational effectiveness. By utilizing this sophisticated service, healthcare organizations can gain invaluable insights and improve their decision-making capabilities, ultimately leading to better outcomes for patients. This transformative approach represents a significant leap forward in how health data can be leveraged for greater efficiency and efficacy in medical practices.

Media

Media

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend
Azure AI Foundry

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend
Azure AI Foundry

API Availability

Has API

API Availability

Has API

Pricing Information

$45/month
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

Hathr AI

Date Founded

2023

Company Location

United States

Company Website

hathr.ai

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/comprehend/medical/

Categories and Features

Categories and Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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