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features
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support

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Description

1upHealth serves as a cutting-edge data platform that promotes efficient health data interchange and interoperability. Utilizing the Fast Healthcare Interoperability Resources (FHIR) standard, it provides cloud-based, API-driven solutions designed for organizations such as payers, healthcare providers, digital health innovators, and life sciences entities. The platform ensures the secure and compliant exchange of high-quality, comprehensive, and timely health data, adhering to CMS interoperability standards while facilitating clinical and claims data aggregation, patient-mediated data sharing, and health data analytics. Among its diverse range of offerings are the 1up FHIR Platform for effective data acquisition and management, 1up Comply to ensure adherence to regulatory standards, and 1up Patient Connect, which empowers individuals to access and share their health information. Additionally, 1up Population Connect allows for the importation of patient population data from leading electronic health records (EHRs), while 1up Exchange streamlines data sharing at the population level, enhancing the overall efficiency of healthcare delivery. Together, these products create a robust ecosystem to advance health data interoperability and improve patient outcomes.

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.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

AWS AI Services No 
AWS App Mesh No 
Amazon Comprehend No 

Integrations

AWS AI Services Yes 
AWS App Mesh Yes 
Amazon Comprehend Yes 

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 Yes 
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 Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

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

Vendor Details

Company Name

1upHealth

Founded

2017

Country

United States

Website

1up.health/

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/comprehend/medical/

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 

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