Reinventing Clinical Data Operations with Generative AI-Enabled Intelligent Document Processing

Challenge

The client managed more than 100 million clinical documents – mainly, unstructured clinical notes and pathology reports in inconsistent formats – essential for running tests, supporting providers, and enabling biopharma R&D. Yet the workflows for interpreting these documents were almost entirely manual. Abstractors spent over 90 minutes per patient reviewing records, extracting key clinical attributes and entering them into “dictionary” spreadsheets, making it impossible to scale operations without growing headcount.

As document volumes continued to increase, workflows became even more fragmented across intake, accessioning, data entry, and billing. Critical clinical information remained locked inside unstructured files, slowing downstream insights for clinicians and limiting the speed at which biopharma partners could build cohorts and prepare validated datasets for clinical trials.

Manual clinical data workflows created an enterprise-wide bottleneck:

• Slower turnaround times
• Rising operational cost
• Data inaccessible for analytics
• Inability to adopt AI for scale

The client envisioned a radical transformation of its clinical data operations, turning millions of unstructured documents into trusted, analytics- & AI-ready data at scale, without compromising accuracy and clinical integrity.

Solution

Provectus partnered with the client to deliver an intelligent document processing (IDP) capability, deployed as a part of its Generative AI Platform, to automate the extraction, structuring, and validation of clinical document data at scale. At the core of IDP is a state-of-the-art, LLM-powered workflow that indexes unstructured clinical documents and automatically populates a standardized data dictionary of more than 50 patient-level attributes.

To replace slow, manual abstraction, Provectus designed and built a production-ready pipeline on AWS that ingests documents from diverse sources (e.g. EMRs, provider uploads, APIs, internal systems), then processes and indexes them leveraging OCR (Amazon Textract), LLMs (Amazon Bedrock, with Anthropic’s Claude 3.5 Sonnet), and contextual reasoning. Each output is stored as structured, traceable data in a vectorized format and is linked back to the source text in the original document, ensuring accuracy and auditability.

A human-in-the-loop (HITL) interface enables abstractors to validate or correct extracted attributes instantly, reducing rework and supporting clinical-grade reliability. By unifying previously fragmented workflows and centralizing document intelligence in a reusable vectorized store, the IDP capability enables more accurate search, speeds up cohort discovery, and unlocks data for analytics and other downstream applications.

Outcome

Provectus delivered a clinical-grade IDP capability that helped to reshape the client’s clinical data operations, connecting data across labs, billing, and customer service. By automating attribute extraction from clinical documents and providing a seamless HITL review, IDP enabled the client to reduce abstraction time from more than 90 minutes to under 20 minutes per patient, significantly increasing operational throughput without adding headcount.

Structured, clinical-grade, validated data now flows directly into downstream R&D, analytics and biopharma workflows, allowing teams to build cohorts faster and deliver higher-quality datasets to trial sponsors. Providers benefit from earlier, cleaner insights that support treatment decisions and reduce delays in patient care. Internally, the client has gained a scalable, repeatable approach to transforming unstructured clinical documents into analysis-ready data, improving accuracy and consistency, accelerating document management, and elevating the overall customer experience.

As a part of the client’s Generative AI Platform, the IDP capability has become a foundation for future agentic automation and GenAI-driven, workflow-centered use cases, enabling the client to extract more value from its clinical data and strengthen the impact of its diagnostic and testing offerings.