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What is Consensus Clarity?

Despite technological progress, many healthcare organizations continue to depend on antiquated, non-automated, and unstructured formats like paper faxes and PDFs for their data management needs. The persistent challenge of achieving seamless interoperability remains a critical issue across diverse healthcare systems. To combat this problem, Consensus Clarity harnesses the power of natural language processing (NLP) and artificial intelligence (AI) to improve data sharing, enhance information visibility, boost workflow efficiency, and optimize resource management among all stakeholders in the healthcare industry. By converting unstructured digital documents into useful and actionable data, Consensus Clarity fosters quicker and more effective communication. Their innovative NLP and AI solutions specifically target the most urgent interoperability challenges faced by the healthcare sector today. Additionally, Clarity methodically removes barriers and enhances resource utilization throughout the entire care continuum. When faced with difficult-to-interpret documents, Clarity is equipped to convert unstructured data into a structured JSON format, allowing for seamless integration with other systems, which significantly improves operational efficiency. This cutting-edge methodology not only simplifies processes but also plays a crucial role in delivering enhanced patient care. In this way, the potential for a more interconnected healthcare landscape becomes increasingly attainable.

What is Amazon Comprehend Medical?

Amazon Comprehend Medical is an NLP service designed to adhere to HIPAA standards, employing machine learning to extract health information from medical documents without necessitating any prior expertise in machine learning from its users. A vast amount of healthcare data is found in unstructured formats, such as physicians' notes, clinical trial reports, and patient histories. Relying on traditional, manual methods for data extraction is not only time-consuming but also prone to errors, as rule-based automation often fails to capture essential contextual details, resulting in incomplete data retrieval. This lack of reliability can significantly undermine the effectiveness of large-scale analytics, which are critical for advancements in the healthcare and life sciences industries, ultimately impeding potential enhancements in patient care and operational effectiveness. By utilizing this sophisticated service, healthcare organizations can gain invaluable insights and improve their decision-making capabilities, ultimately leading to better outcomes for patients. This transformative approach represents a significant leap forward in how health data can be leveraged for greater efficiency and efficacy in medical practices.

Media

Media

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend
JSON
eFax Corporate

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend
JSON
eFax Corporate

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Consensus Cloud Solutions

Company Location

United States

Company Website

www.consensus.com/clarity/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/comprehend/medical/

Categories and Features

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

Categories and 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

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