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Average Ratings 0 Ratings

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ease
features
design
support

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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

Ellipsis Health has developed an innovative care management platform powered by AI, featuring its virtual assistant, Sage, which aims to streamline and improve patient engagement through voice interactions that prioritize emotional intelligence while seamlessly fitting into existing clinical processes. Sage is capable of conducting completely autonomous phone conversations in multiple languages with patients, managing various tasks like enrolling in programs, verifying eligibility, checking copays, and responding to inquiries, in addition to carrying out assessments such as health risk evaluations, follow-up communications after discharge, satisfaction surveys, and tracking outcomes. This platform enhances clinical operations by facilitating care coordination, monitoring treatment adherence, and performing check-ins before and after discharges, thus aiding healthcare providers in ensuring uninterrupted care and boosting quality metrics. At the core of this system is an "empathy engine," which evaluates vocal biomarkers—such as tone, pace, and speech patterns—to identify emotional and mental health indicators, thereby providing valuable insights into patient wellbeing. Through these advanced capabilities, Sage not only assists in operational efficiency but also fosters a deeper connection between patients and healthcare practitioners, ultimately contributing to better health outcomes.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS AI Services
AWS App Mesh
Amazon Comprehend

Integrations

AWS AI Services
AWS App Mesh
Amazon Comprehend

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/comprehend/medical/

Vendor Details

Company Name

Ellipsis Health

Founded

2017

Country

United States

Website

ellipsishealth.com

Product 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

Product Features

Alternatives

Alternatives

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