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Description

Amazon Comprehend Medical is a natural language processing (NLP) service compliant with HIPAA that leverages machine learning to retrieve health information from medical texts without requiring any prior machine learning expertise. A significant portion of health data exists in unstructured formats such as physician notes, clinical trial documentation, and patient medical records. The traditional approach of manually extracting this data is labor-intensive and inefficient, while automated methods based on strict rules often overlook crucial contextual details, leading to incomplete data capture. Consequently, this limitation results in valuable information remaining untapped for large-scale analytical efforts that are essential for progressing the healthcare and life sciences sectors, ultimately impacting patient care and operational efficiencies. By addressing these challenges, Amazon Comprehend Medical enables healthcare professionals to harness their data more effectively for better decision-making and innovation.

Description

Truveta operates as a health data and analytics platform focused on leveraging information to enhance patient lives. By consolidating de-identified electronic health records from more than 30 healthcare systems, it provides researchers with access to a rich array of patient data, which includes clinical notes, imaging, and genomic information. This vast repository represents data from upwards of 120 million patients, giving a well-rounded perspective on healthcare delivery throughout the United States. The Truveta Studio, which serves as the platform's analytics hub, equips researchers with sophisticated tools like notebooks and dashboards, all maintained within a secure and HIPAA-compliant framework. With daily updates to its data, the platform guarantees that insights into patient care and outcomes are both current and relevant. Furthermore, Truveta's dedication to data integrity is showcased by its implementation of the Truveta Language Model, an advanced AI framework that efficiently translates EHR data into precise and reliable data points for the advancement of medical research. This commitment not only enhances research capabilities but also fosters a deeper understanding of healthcare trends and patient needs, ultimately contributing to improved health outcomes.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS AI Services Yes 
AWS App Mesh Yes 
Amazon Comprehend Yes 

Integrations

AWS AI Services No 
AWS App Mesh No 
Amazon Comprehend No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/comprehend/medical/

Vendor Details

Company Name

Truveta

Founded

2020

Country

United States

Website

www.truveta.com

Product Features

Data Extraction

Disparate Data Collection No 
Document Extraction No 
Email Address Extraction No 
IP Address Extraction No 
Image Extraction No 
Phone Number Extraction No 
Pricing Extraction No 
Web Data Extraction No 

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

Alternatives

Alternatives

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