Dual-sided Oncology EHR platforms

Date: July 8, 2025

Some oncology data platforms today operate on a dual-sided model: they collect structured EHR data from point-of-care, and also offer analytics back to providers and life sciences.

It’s a compelling approach—direct capture plus feedback loop.

The data often has advantages over traditional open claims and closed payer datasets:
- Cleaner diagnosis and staging fields
- More reliable biomarker capture
- Closer visibility to practice patterns

But coverage is narrow, and depth depends on how well the EHR is used. Unlike open claims (broad, longitudinal, anonymized), or closed payer data (linked to cost), these platforms offer real clinical detail; but within a fragmented footprint.

They use traditional ML today for risk modeling, quality flagging, cohort building, and prediction.

But LLMs could help them:
- Make unstructured clinical notes searchable
- Auto-tag patient journeys with context
- Standardize narratives across diverse inputs

That’s where generative AI feels promising: not just for answers, but for metadata and explainability layers.

There’s a lot of potential here. But we’re still early.

Curious if others are seeing LLMs being used to bridge the gap between structured EHR data and claims-based longitudinal analytics.

#HealthcareData #Oncology #RealWorldEvidence #GenerativeAI #DataStrategy #PharmaAnalytics