COVID-19 Response
Zio Service provides
comprehensive support for telemedicine
Patients can receive and apply Zio monitors from their homes.
See how Zio Service easily fits into your telemedicine workflows.
THE current problem
Early detection of heart rhythm disorders, such as atrial fibrillation (“AF”) and other clinically relevant arrhythmias, allows for appropriate medical intervention and helps avoid more serious downstream medical events, including stroke.
people in the UK suffer with AF
to have a stroke if AF is undiagnosed and not managed
Arrhythmias have a £2.2b financial impact on healthcare systems
Source: Transforming services: a systems based approach. Public Health England. https://publichealthmatters.blog.gov.uk/2016/07/14/transforming-services-a-systems-based-approach/ Published July 14, 2016. Accessed May 1, 2018.
Proven history
Our innovation timeline
2006
Company Founded at Stanford Biodesign Center
2009
First-Gen Zio Patch Receives FDA Clearance
2011
Commercial Launch of Zio XT
2013
First (of 20+) Peer-Reviewed Clinical Studies Published
2016
Initial Public Offering on NASDAQ
2019
Over 2 million patients treated
2008
Acquisition of iCardia
2010
iRhythm Cited by White House
2012
First of 13 Patents Issued to iRhythm
Second-Gen Zio Patch Receives FDA Clearance
2014
ISO and CE Mark Certification
2017
Zio AT Monitoring System Receives FDA Clearance
One Millionth Patient Treated
With over 20 peer reviewed publications and over 2 million patients, we are a proven monitoring solution. With our rich heritage stemming from Stanford Biodesign, we are a trusted partner for your cardiac ambulatory monitoring service.
Our solution
Continuous, uninterrupted monitoring
Zio Xt Patient-friendly monitor
The Zio service was designed specifically to facilitate high patient compliance and allow data to be recorded uninterrupted and continuously for up to 14 days.
No gaps in data due to patient-centric design
Convenient button to mark episodes
Patients can continue daily activities while wearing the Zio monitor, including showering and exercising.
Artificial Intelligence (AI) and our Zio deep learning algorithm
Developed in collaboration with the Stanford Machine Learning Group, our deep neural network can detect and identify 10 arrhythmia classes. Combining deep learning and classic machine learning capabilities with our expert cardiac technician review, Zio by iRhythm gives physicians and patients the assurance of expert-level accuracy in arrhythmia detection.
10 arrhythmias classified
Faster diagnosis and accelerated patient care
Faster diagnosis and accelerated patient care
