Agile pain-related insights for clinics and home care
*Currently for investigational use only

Quantifying the physiological impact of pain to improve control, compliance and outcomes

On our path to improving pain care across diverse care settings, we are harnessing our AI capabilities with NOLedge™ – an agile physiological insights platform. 

By transforming physiological data from multiple sources into pain-related insights, Medasense is positioned to lead prediction, patient stratification and prevention strategies.

NOLedge™ for pain control by Medasense | Agile pain-related insights for clinics and home care

Cloud-Based Platform

Our wireless finger probe collects patient data, which is processed and analyzed in the cloud.

Personalized Insights

Pain insights enabling personalized and optimized treatment are generated and displayed in an app.

Customized Interface

Tailored user interface to meet the needs of each clinical setting and the individual user.

A game changer in clinic and home-based care

For Patients

Providing objective feedback on individual pain trends and therapy progress

Improving self-management and treatment compliance

Regulating patient anxiety

For Providers

Providing objective information for effective decision making

Streamlining patient status tracking follow up on patient status and treatment effectiveness

Managing internal performance

For Payers

Standardizing patient pathways, reducing variability

Enabling a smarter reimbursement scheme

The Physiological Principles Behind NOL

NOL is based on a proprietary, patented artificial intelligence technology that quantifies the individual physiological response to pain in real-time, continuously and noninvasively.

Activation of the sympathetic nervous system, as a result of multiple stimuli and inputs, leads to a constellation of nociception-related physiological responses, with complex inter-associations and different response profiles. Recognizing the complex nature of this process, the Nociception Level (NOL) index was developed as a multiparameter composite of autonomic signals.