Project portfolio · deep dive
From clinical noise to trial-ready cohorts
ConcertAI held billions of unstructured patient documents — nurse notes, phone calls, lab reports, imaging, pharmacy and billing records — for 4 million oncology patients across four decades. Genome360 mined that text to extract genomic biomarker results and surface exactly which patients qualify for which clinical trials — the data pharma pays the most for.
The solution, in three parts
The outcome
What automation bought — time, on a log scale
Detail — how these are estimated
Per-unit assumptions, deliberately conservative: 15s to open and classify one document, 30s to read and transcribe one biomarker key-value pair (× 800 ≈ 6.7h per report), 5min to verify a pre-extracted report in the human-in-the-loop app. Over a corpus of ~4B documents yielding ~30K NGS documents:
- No automation = screen 4B docs (15s) + extract 30K reports (800 × 30s) ≈ 16.9M hrs — screening dominates.
- Step 1 only = classifier surfaces the reports; humans extract 30K (6.7h) ≈ 200K hrs.
- Steps 1 + 2 = humans only verify 30K pre-filled reports (5min) ≈ 2.5K hrs.
The context — ConcertAI
What it is
Its customers
Its suppliers
My role
I owned the algorithms pipeline end-to-end — the cascade that finds NGS reports and the NLP that reads them — which made me the cross-team point of contact and pulled me into adjacent surfaces (a custom OCR pipeline, the validation-app backend).