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"An Ingenious Use of AI to Identify Patients with Gastroparesis"

Gastroparesis is hard to diagnose due to overlapping symptoms and inconsistent coding. Eversana Intouch applied machine learning to define clinical profiles, uncovering nearly 1.8M additional patients. Learn how this data-driven approach accelerates diagnosis and transforms patient identification.

Gastroparesis is notoriously hard to diagnose—symptoms overlap, and patients are often misclassified in claims and EHR data.

A mid-sized pharmaceutical company needed to identify candidates for a newly approved nasal spray formulation of metoclopramide.

Eversana Intouch analyzed open claims data using both supervised and unsupervised machine learning. By comparing patient profiles to a control group and stratifying by disease severity, we built predictive models that surfaced nearly 1.8 million additional patients likely to benefit from treatment.

Our unique blend of clinical expertise, advanced analytics, and AI-powered tools — combined with strategic data partnerships and ACTICS by Eversana — delivers a deeper, more holistic view of patient cohorts and helps brands identify hidden patients faster, with measurable impact.

Discover how this ingenious use of AI is redefining patient identification — and delivering impact
where patients and brands need it most.

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