NLP for Clinical Documentation
Structured insight from unstructured clinical text.
Extract structured, analysis-ready data from unstructured clinical notes, adverse event narratives, medical records, and literature using state-of-the-art natural language processing — purpose-built for the vocabulary and regulatory context of life sciences.
The majority of clinically meaningful information in healthcare exists in unstructured text — physician notes, adverse event narratives, discharge summaries, medical literature, and patient-reported accounts. This data is largely inaccessible to traditional analytics systems, which require structured inputs. Bracy Analytics applies modern NLP and large language model techniques — fine-tuned on clinical and biomedical corpora — to extract structured, analysis-ready information from this unstructured content. Our NLP platform is built for the specific vocabulary, coding systems, and regulatory requirements of life sciences: MedDRA, SNOMED CT, ICD-10/11, and the adverse event reporting standards of FDA and EMA. The result is faster medical coding, more complete safety signal detection, and analytical access to data that was previously locked in text.
What We Deliver
Named Entity Recognition for Clinical Concepts
Fine-tuned NER models that identify and classify clinical entities — diseases, drugs, procedures, adverse events, and biomarkers — mapped to MedDRA, SNOMED CT, ICD-10/11, and RxNorm coding systems.
Adverse Event Narrative Coding & Signal Detection
Automated coding of adverse event narratives to MedDRA preferred terms and system organ classes — with signal detection algorithms that identify emerging safety patterns across large narrative datasets.
Medical Coding Assistance & Automation
AI-assisted medical coding tools that suggest MedDRA and ICD codes from clinical text, with human-in-the-loop review workflows and audit trails for regulatory compliance.
Clinical Summarization & Record Abstraction
Automated abstraction of key clinical information from EHR notes, discharge summaries, and medical records — extracting structured data elements for research, quality measurement, and regulatory reporting.
Literature Mining & Evidence Synthesis
NLP pipelines that extract clinical evidence from published literature — identifying relevant studies, extracting efficacy and safety data, and synthesizing evidence for regulatory submissions and systematic reviews.
Automated Reporting from Unstructured Sources
End-to-end pipelines that transform unstructured clinical text into structured datasets, summary reports, and regulatory outputs — reducing manual abstraction burden and improving data completeness.
Real-World Applications
A pharmaceutical company needs to code 50,000 adverse event narratives from a global Phase III trial for safety database submission.
NLP-assisted coding pipeline processes narratives at 10x manual speed with 94% accuracy, with human review focused on low-confidence cases — reducing coding time from 6 weeks to 4 days.
A health system needs to abstract structured data from 200,000 physician notes for a population health analytics program.
Clinical NLP pipeline extracts diagnoses, medications, procedures, and lab values from unstructured notes, creating a structured dataset that enables population-level analytics previously impossible.
A biotech company needs to mine published literature to identify all reported adverse events for a drug class in support of a safety update.
Literature mining pipeline processes 15,000 publications, extracting and structuring adverse event data that would have required 6 months of manual review — completed in 2 weeks.
A digital health company needs to extract clinical endpoints from patient-reported text data collected through a mobile app.
Custom NLP model trained on patient-reported language extracts clinically meaningful endpoints from app data, enabling RWE generation from a previously unstructured data source.
Ready to Discuss NLP for Clinical Documentation?
Our team will assess your program context and design a solution scoped to your data environment, regulatory obligations, and timelines.