OM1 Launches AI-Powered Digital Phenotypes Platform, Creating “Fingerprints” for Patients

What You Should Know:

OM1, a real-world data, outcomes and technology company with a focus on chronic conditions, launches PhenOM™, its artificial intelligence-powered platform for personalized medicine.

Calibrated using OM1’s longitudinal health history datasets, PhenOM uses AI to identify unique digital phenotypes associated with conditions and outcomes and compare patients’ records to them to highlight risks and opportunities. PhenOM powers personalized healthcare insights at scale across the entire healthcare ecosystem, from life science research to point-of-care clinical decision-making.

Creating digital phenotypic ‘fingerprints’

PhenOM is an AI that gathers detailed information from large patient datasets – including unstructured clinical notes and disease activity metrics – to isolate unique patterns associated with patients with distinct characteristics. PhenOM synthesizes these patterns into unique digital phenotypes or ‘fingerprints.’ These phenotypes can be studied to inform research and deployed in real-world datasets – inside and outside OM1 – as well as in health systems and through standalone patient-facing web portals.

PheonOM was built with expert clinician oversight. With an overarching mission to improve individual care and population health, the platform powers five solutions that address pain points across the healthcare system:

• Diagnostic Insights: Finding patients with rare, undiagnosed, or misdiagnosed conditions, including subgroups of interest
• Treatment Insights: Personalizing treatment recommendations to improve access to care and individualizing treatment selection
• Risk Insights: Predicting risk of specific negative outcomes, including disease progression, complications, and catastrophic events
• Utilization Insights: Enabling focus on patients at risk for higher utilization, and utilization growth, over time
• Clinical Trial Insights: Accelerating trial startup and recruitment by identifying patients most likely to meet qualifications for enrollment, including those potentially overlooked

artificial intelligence
ai
personalized medicine
healthcare
medicine