Clinical Data Operations at 4.5x Throughput With Agentic Document Intelligence

The client is a global cancer-diagnostics company that combines molecular biology with bioinformatics to deliver non-invasive tests that guide treatment decisions. The volume of clinical documentation behind those tests — hundreds of millions of unstructured notes and pathology reports — was the bottleneck.

01 The Challenge

Hundreds of millions of clinical documents, read by hand, one patient at a time

Abstractors spent hours per patient reviewing records, extracting key clinical attributes, and typing values into “dictionary” spreadsheets. The workflow fragmented further across intake, accessioning, data entry, and billing. Critical clinical intelligence stayed locked inside unstructured files — slowing the turnaround for clinicians and limiting the speed at which biopharma partners could build cohorts for trials.

100M+

Clinical documents

Managed manually across fragmented workflows before the engagement

Headcount growth was not the answer. A clinical-grade pipeline was.

02 The Approach

Agents that read documents. Abstractors that review agents.

Provectus delivered AI agents as part of the client’s internal GenAI Platform. Each agent indexes unstructured clinical documents and populates a standardized data dictionary of 50+ patient-level attributes — per document, per patient, traced back to the source region.

The rule was clinical-grade reliability first, throughput second. A human-in-the-loop (HITL) interface lets abstractors validate or correct extracted attributes on the spot. Every correction feeds model calibration. Nothing ships to the data warehouse until a human signs off.

03 The Build

Textract plus Claude 3.5 Sonnet plus a vector store that answers R&D questions

The pipeline ingests documents from EMRs, provider uploads, APIs, and internal systems. OCR runs on Amazon Textract. Contextual reasoning runs on Amazon Bedrock, backed by Anthropic’s Claude 3.5 Sonnet. Each extracted attribute is stored in a vectorized format with a link back to the exact source passage.

The reusable vector store matters beyond the immediate workflow: R&D can now search across the whole corpus, and biopharma partners can build cohorts from documents that used to be inaccessible.

04 The Results

4.5x faster abstraction per patient. Throughput up without headcount up.

4.5x

Per-patient clinical-data abstraction speed

Measured against the manual baseline

Document processing throughput climbed ten-fold in critical workflows. Abstraction per patient moved 4.5x faster. The internal R&D and data teams build cohorts faster; biopharma sponsors receive higher-quality datasets for clinical-trial design; ordering clinicians get cleaner insights earlier.

The pipeline is now the reusable foundation for the next set of document-heavy workflows on the client’s GenAI Platform.

05 What’s Next

A foundation other document-heavy HCLS workflows can run on

The agent pattern — OCR plus Bedrock plus vector store plus HITL cockpit — is one of the document-intelligence shapes the Evidence Lens blueprint Provectus now offers to other HCLS organizations builds on. The next diagnostics or pharma client starts from the tuned baseline this engagement produced.