Clinical NLP Pipeline Engineering

Automate the conversion of unstructured physician notes, discharge summaries, and medical literature into structured, query-ready data. We deliver pipelines with >95% accuracy for key medical concepts (problems, medications, procedures) using models like BioBERT and ClinicalBERT, enabling population health analytics and clinical research.

Reduce patient screening time from weeks to hours. Our NLP pipelines rapidly parse millions of clinical documents to identify eligible patients based on complex inclusion/exclusion criteria, directly integrating with systems like Epic or Cerner. This accelerates study enrollment and time-to-market for new therapies.

Surface critical patient insights at the point of care. Our pipelines extract and contextualize data from notes to power real-time clinical alerts for conditions like sepsis risk or medication contradictions, feeding directly into your EHR workflow without disrupting clinician focus.

Automate the abstraction of data for quality measures (e.g., CMS, Joint Commission) and adverse event reporting. Our systems ensure consistent, audit-ready data extraction, reducing administrative burden and improving compliance accuracy for value-based care programs.

Construct comprehensive, timeline-based patient representations from narrative text. Our pipelines link extracted entities (symptoms, diagnoses, treatments) across encounters to build rich phenotypes for retrospective research, predictive modeling, and personalized care pathway discovery. Learn more about our approach to Clinical Knowledge Graph Development.

Create secure, research-ready datasets. We implement automated de-identification pipelines using named entity recognition and surrogate generation, stripping Protected Health Information (PHI) with >99% recall to enable safe internal AI development and collaboration. This foundational work supports advanced initiatives like Medical Domain-Specific Model Training.

Secure, automated pipelines for ingesting and de-identifying PHI from EHRs, physician notes, and discharge summaries using NIST-validated frameworks. Ensures data privacy from day one.

Deploy models trained on clinical corpora (e.g., MIMIC-IV) to extract and map entities like medications, conditions, and procedures to standard ontologies (SNOMED CT, RxNorm) with >95% accuracy.

Engineer systems to identify temporal relationships, negation, and clinical intent (e.g., 'family history' vs. 'patient condition'), creating a structured patient narrative from free text.

Architect containerized, Kubernetes-orchestrated pipelines with real-time performance monitoring, drift detection, and automated retraining to maintain accuracy across millions of documents.

Seamlessly integrate extracted structured data back into EHR systems (Epic, Cerner) and analytics platforms, enabling real-time decision support and population health management without disrupting clinician workflow.

Implement a continuous validation framework with clinician-in-the-loop feedback, algorithmic fairness audits, and comprehensive audit trails to ensure model safety and compliance with FDA SaMD guidelines.