Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Facilitate connections between healthcare entities such as hospitals, clinics, health plans, and life sciences with app developers and health data partners to create innovative digital services based on FHIR APIs. Enhance both the efficiency and safety of transitions throughout the continuum of care, whether in-patient or out-patient. Offer personalized wellness and prevention strategies tailored to at-risk individuals, fostering proactive health management. Encourage collaboration among patients, healthcare providers, and physicians to effectively address chronic conditions, leading to better management outcomes. Focus on patient-centered digital services that prioritize user experience and safety, while minimizing risks during care transitions. Utilize an enterprise-grade platform capable of managing, securing, and scaling APIs that remain agnostic to FHIR servers. Seamlessly integrate healthcare data from various sources, including internal systems, external partners, or open-source FHIR-ready resources. By swiftly launching digital services like mobile applications, advance the vision of patient-centric healthcare and enhance data interoperability, ultimately improving healthcare delivery for all. This approach not only enhances patient engagement but also drives innovation across the healthcare landscape.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
AWS AI Services
Yes
AWS App Mesh
Yes
Amazon Comprehend
Yes
Apigee
No
Google Cloud Platform
No
Integrations
AWS AI Services
No
AWS App Mesh
No
Amazon Comprehend
No
Apigee
Yes
Google Cloud Platform
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
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
Yes
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)
No
In Person
Yes
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/comprehend/medical/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/solutions/apigee-health-apix
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