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Description
Clin'form is an adaptable and powerful electronic Clinical Outcome Assessment (eCOA) platform that aims to simplify clinical trials for both patients and research sites, improve data integrity, and speed up processes for research teams. Its versatility allows it to be used across various protocols and demographics, making it suitable for any type of questionnaire—including Patient-Reported Outcomes (PRO), Observer-Reported Outcomes (ObsRO), Clinician-Reported Outcomes (ClinRO), and diaries—while catering to individuals of all ages, from children to the elderly. The platform is designed to handle diverse population sizes, effectively managing studies that involve anywhere from a small group to over 10,000 participants spread across multiple regions worldwide. Clin'form integrates smoothly with other clinical systems to provide an efficient workflow, supporting various data collection methods such as dedicated tablets or smartphones, the patient's personal device, web access on any browser, and in-person interviews. Noteworthy features include an integrated tele-visit option to accommodate unexpected changes and patient engagement tools that promote high levels of compliance. Additionally, Clin'form's user-friendly interface ensures that both researchers and participants can easily navigate the platform, making the clinical trial experience more streamlined and effective.
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
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
Kayentis
Founded
2003
Country
France
Website
kayentis.com/clinform-ecoa-solution/
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