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What is InQuery?

InQuery is redefining medical record review for insurers, attorneys, and healthcare experts with AI-driven automation designed for precision, speed, and compliance. Instead of manually sifting through hundreds of pages, users can now review, organize, and summarize complex medical files in minutes. The platform’s machine learning engine automatically indexes and labels every document by provider, type, and relevance while extracting ICD codes, medications, and treatment data. These insights are compiled into customizable case summaries and chronologies, enabling users to quickly identify patterns, key facts, and missing information. With InQuery, organizations can handle three times more cases with the same resources—cutting costs, reducing turnaround time, and improving decision quality. Automated quality checks ensure compliance and consistency, flagging regulatory or data gaps before they impact outcomes. The platform empowers teams to focus on high-value analysis rather than repetitive document handling. Its one-click export feature generates ready-to-use, fully organized case files, saving hours of preparation. Designed with strict adherence to HIPAA and SOC 2 standards, InQuery safeguards sensitive information with robust security and reliability. It’s the intelligent, modern solution for transforming medical record chaos into clarity and confidence.

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

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Media

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Comprehend

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

InQuery

Date Founded

2024

Company Location

United States

Company Website

www.inquery.ai/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/comprehend/medical/

Categories and Features

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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