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features
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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

Sales teams in logistics and wholesale are experiencing significant growth due to the integration of decision intelligence and ERP automation. Sales managers at prominent B2B companies in the SME sector are turning to acto to streamline their operational processes. The B2B sales landscape is saturated with various sources of information and manual tasks stemming from ERP, CRM, BI, and Excel spreadsheets. In the absence of effective prioritization, a staggering 80% of valuable insights go unutilized, causing sales teams to miss out on vital opportunities, potential risks, and customer anomalies. This overwhelming influx of data leads to an incessant quest for accurate answers, leaving a large portion of crucial insights untapped. Additionally, the manual searches that sales teams engage in often result in repetitive tasks, such as drafting quotes, interacting with customers, and entering orders, which further compounds inefficiency. Consequently, the reliance on unused data and manual operations can diminish the time allocated to engage with key customers, ultimately costing businesses up to 15% in potential sales revenue. By leveraging AI-driven analyses and automating ERP processes, organizations can significantly boost their B2B sales efficiency. This transformation not only enhances productivity but also empowers sales teams to capitalize on the insights that truly matter.

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 
Microsoft Excel No 

Integrations

AWS AI Services No 
AWS App Mesh No 
Amazon Comprehend No 
Microsoft Excel 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 No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

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

acto

Country

Germany

Website

www.heyacto.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 

Product Features

Decision Support

Application Development No 
Budgeting & Forecasting No 
Data Analysis No 
Decision Tree Analysis No 
Monte Carlo Simulation No 
Performance Metrics No 
Rules-Based Workflow No 
Sensitivity Analysis No 
Thematic Mapping No 
Version Control No 

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