Portfolio Accelerator · Healthcare · Open Source
Clinical NLP on Open-Source AI: An Implementation Blueprint for Healthcare Systems
A self-hosted clinical NLP architecture built on open-source AI, no proprietary license, no cloud dependency, designed around PHI protection from day one.
problem
Why Health Systems Are Moving Clinical NLP Off Proprietary Black Boxes
A proprietary clinical NLP engine locks a health system into per-seat licensing, opaque model behavior, and a vendor's roadmap. A CIO evaluating clinical NLP open source AI wants an architecture that is inspectable, portable, and doesn't require sending clinical text to a third-party API.
demo
Inside the Accelerator: What the Clinical NLP Open-Source Design Actually Does
This page shows Kriv AI's clinical NLP open-source accelerator. In the interest of transparency: the environment and infrastructure (Docker Compose, all services) are set up; the NLP pipeline itself (PHI detection, entity extraction, note classification) is designed but not yet built out. We're presenting this honestly as an architecture blueprint.
Pipeline: Ingestion, De-identification, Entity Extraction, Coding Suggestions
The designed data flow: a clinical note enters Presidio for PHI detection and de-identification, the de-identified text passes to scispaCy for named-entity extraction (medications, diagnoses, procedures), a note classifier identifies the document type (discharge, radiology, pathology, progress note), a local LLM via Ollama summarizes the note without sending any PHI to an external API, and the structured result exports as a FHIR DocumentReference resource, with every step logged to PostgreSQL for audit.
The Open-Source Stack We Deployed, and Why Each Piece Was Chosen
Presidio for PHI detection, scispaCy for clinical named-entity recognition, Ollama for local LLM summarization (Llama 3 or Meditron, no external API calls), PostgreSQL with pgAudit for the audit trail, MinIO for object storage, and Superset for dashboards, all orchestrated through Docker Compose so the entire stack can run air-gapped after initial setup. No piece of this stack carries a per-seat proprietary license.
status
What's Real Today, and What's Roadmap
Honestly: the environment setup is complete, 22 reference repositories have been reviewed, and the docker-compose architecture (7 services) is fully specified. The PHI-detection pipeline, the entity-extraction pipeline, note classification, FHIR export, and the audit dashboards are designed but not yet built out and exercised against synthetic clinical notes. We'll update this page with real, measured accuracy and de-identification numbers once the pipeline is running end to end.
proof
Build-Demo-Vanish: How Kriv AI Proves This Before You Buy It
Every Kriv AI accelerator follows the same Build-Demo-Vanish pattern: build a working proof of concept on synthetic data and real infrastructure, demonstrate it to a prospect, then tear it down. Big 4 firms sell a slide deck and a roadmap for this kind of build. Once this pipeline is running, a health system CIO will be able to watch it process a synthetic clinical note end to end, not read about it in a proposal.
compliance
Where Clinical NLP Open Source Fits in a HIPAA-Aligned Architecture
The architecture is designed around the same PHI-handling discipline HIPAA's technical safeguards expect: PHI never reaches the local LLM (only de-identified text does), every processing step is logged for audit, and because the whole stack is self-hosted, clinical text never leaves the health system's own infrastructure, unlike a cloud-API-based NLP vendor.
comparison
Open Source vs. Proprietary Clinical NLP Platforms: A Buyer's Comparison
Licensing: open source carries no per-seat fee, versus recurring per-seat or per-note costs for proprietary engines. Data portability: the self-hosted stack and its output format belong to the health system outright, versus vendor lock-in on a proprietary platform. Auditability: every open-source component's behavior can be inspected line by line, versus a proprietary vendor's black-box model. Deployment: the open-source stack can run fully air-gapped after setup, versus most proprietary engines requiring an ongoing cloud API connection.
audience
Who This Accelerator Is Built For
Built for CIOs and clinical informatics leaders at health systems evaluating whether to build clinical NLP on open-source infrastructure they own outright, rather than licensing a proprietary engine or sending clinical text to an external API.
engagement
From Accelerator to Production: Engagement Path
A scoped engagement builds this pipeline out against your real clinical note volume and infrastructure, carrying the same self-hosted, no-vendor-lock-in architecture through from day one.
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Straight answers
Frequently asked questions about Clinical NLP on Open-Source AI: An Implementation Blueprint for Healthcare Systems
Is there a free, open source clinical NLP pipeline available today?
Kriv AI's clinical NLP open-source accelerator is at the architecture and environment-setup stage: the Docker Compose stack (Presidio, scispaCy, Ollama, PostgreSQL) is specified, but the pipeline itself is not yet built out and exercised. We disclose this honestly rather than overstating readiness.
What is the best open source NLP stack for clinical notes?
The designed stack pairs Presidio for PHI detection, scispaCy for clinical named-entity recognition, and a local LLM via Ollama for summarization, none of which carry a per-seat proprietary license and all of which can run fully self-hosted.
How does open-source clinical NLP compare to proprietary platforms?
No per-seat licensing cost, full data portability since the health system owns the deployed stack outright, line-by-line auditability of every component, and the ability to run fully air-gapped, versus vendor lock-in and black-box behavior on a proprietary platform.
Is any real patient data used?
No. The accelerator, once built, will run entirely on synthetic clinical notes. No real PHI is used at any stage.
How does this handle HIPAA compliance?
The architecture is designed so PHI never reaches the local LLM, only de-identified text does, every processing step is logged for audit, and clinical text never leaves the health system's own infrastructure.
Can Kriv AI build this out for our health system?
Yes. A scoped engagement can build this pipeline out against your real clinical note volume and infrastructure. Contact us to discuss scope and timeline.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
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