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ease
features
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support

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Description

Omda MedSciNet solutions enhance the quality, usability, and administration of medical data throughout its lifecycle from collection to analysis. This secure and standardized software streamlines the process for clinical researchers and other professionals in healthcare and academia, allowing for the efficient gathering and examination of well-organized medical data. The dedicated Omda team excels in the development and management of customized database applications, as well as designing clinical studies tailored for medical research and quality registries. The Omda MedSciNet study platform offers a dependable, feature-rich, and highly adaptable online environment suitable for hosting and managing complex study and clinical trial systems. With a proven track record spanning 20 years, Omda MedSciNet has effectively supported studies and clinical trials of various scopes and sizes. Standard implementations come equipped with a comprehensive array of components essential for launching fully functional studies or clinical trials, ensuring that researchers have all the necessary tools at their disposal. This commitment to quality and customization positions Omda MedSciNet as a leader in the field of medical data management.

Description

Progressing artificial intelligence to remove the need for trial and error in healthcare, our digital twins facilitate swift and assured clinical trials. We focus on areas such as neuroscience, immunology, and metabolic diseases, among others. TwinRCTs expedite full enrollment by requiring fewer participants to provide equivalent statistical power compared to conventional trial methodologies. This approach significantly reduces the time needed for late-stage study enrollment. Additionally, TwinRCTs enhance the ability to detect treatment effects in early-stage studies by bolstering statistical power without necessitating an increase in participant numbers. They enable researchers to make informed decisions based on initial study outcomes and help attract more participants to trials. By utilizing smaller control groups, TwinRCTs also improve participants' odds of receiving the experimental treatment. Our commitment to positioning clinical trials with digital twins for regulatory success is unwavering. Unlearn is at the forefront of transforming the medical field through the innovative application of artificial intelligence, creating and implementing novel generative models that are trained on vast datasets derived from previous patient studies. This evolution in methodology not only streamlines research but also enhances the overall effectiveness of clinical trials.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

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

Omda

Founded

1999

Country

Norway

Website

omda.com/solutions/health-analytics/omda-medscinet/

Vendor Details

Company Name

Unlearn

Country

United States

Website

www.unlearn.ai/

Product Features

Clinical Trial Management

21 CFR Part 11 Compliance
Document Management
Electronic Data Capture
Enrollment Management
HIPAA Compliant
Monitoring
Patient Database
Recruiting Management
Scheduling
Study Planning

Product Features

Clinical Trial Management

21 CFR Part 11 Compliance
Document Management
Electronic Data Capture
Enrollment Management
HIPAA Compliant
Monitoring
Patient Database
Recruiting Management
Scheduling
Study Planning

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