Harness AI-Powered Digital Phenotyping for Actionable Insights with PhenOM™
Creating digital phenotypic ‘fingerprints’
PhenOM™ is an artificial intelligence (AI)-powered digital phenotyping platform trained using OM1’s repository of linked EMR, claims and other data covering more than 300 million patients. The richness of the source data lets these phenotypes capture many complex facets of the target patients’ journeys.
Digital phenotypes are built from complex signals and interactions shared by patients with similar conditions, characteristics, or outcomes that can help distinguish these patients from others. PhenOM isolates these patterns and synthesizes them into unique ‘fingerprints’ that help highlight patients of interest. PhenOM™ runs on patented AI technology and is built for medical explainability and real-world deployment.
Learn how to put PhenOM to use
Answer your most challenging questions with personalized and actionable insights powered by PhenOM
Find patients with rare, undiagnosed, or misdiagnosed conditions, including patient subgroups.
Isolate patients who are more likely to generate higher utilization and utilization growth over time.
Personalize treatment recommendations to improve access to care and individualize treatment selection.
Accelerate recruitment by focusing on patients most likely to meet enrollment criteria.
Predict the risk of specific outcomes like disease progression, complications, and catastrophic events.
Most people have now heard of the promise of AI through GPT-4, but we still have not seen many really mature, real-world clinical applications using advanced language models.
Where PhenOM differs is by using AI to translate specific, actionable insights from our highly-enriched datasets into other data environments at scale, while still maintaining a personalized focus. This capability creates a huge potential for impact, from identifying patients with under-diagnosed conditions to enabling personalized assessments of benefit from particular therapies and accelerating clinical trial enrollment.