Reducing targeting noise by supplementing claims with oncology labs: Metastatic breast cancer case study
A pharmaceutical/life-sciences client found that diagnosis codes alone gave imprecise patient targeting in precision oncology, particularly where therapy eligibility depends on specific biomarker subtypes. The client combined longitudinal claims data with genomic laboratory results to sharpen targeting. This narrowed the initial cohort from 75,395 metastatic breast cancer patients to 1,188 subtype-confirmed patients, and from 9,168 oncologists to 340 actionable targets, enabling more efficient provider engagement ahead of treatment decisions.
The customer is anonymised in HealthVerity’s own case study — not withheld by us.
HealthVerity is a real-world-data marketplace aggregating claims, EHR and lab data from 75+ sources via a proprietary patient-matching layer.
This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.
Published by HealthVerity on its own site — read the original case study on info.healthverity.com ↗
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