Clinical NLP at Scale
Turn unstructured clinical text into searchable, structured data. Extract ICD-10 codes, medications, diagnoses, and clinical observations from medical records using AI-powered NLP pipelines.
Healthcare IT teams, clinical informatics departments, and health systems processing thousands of clinical documents daily
Clinical notes, discharge summaries, and pathology reports contain critical patient information locked in unstructured text. Manual chart review is slow, expensive, and error-prone — clinicians spend hours extracting diagnoses, medications, and procedure codes from free-text records.
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Before & After Mixpeek
Before
Manual chart review
Clinicians spend 2+ hours per case extracting relevant findings
Coding backlog
3-5 day turnaround for ICD-10 code assignment
Keyword search only
Cannot find "heart attack" when note says "acute MI"
Siloed records
No cross-record search for population health queries
After
Automated extraction
Entities extracted in seconds per document
Real-time coding
ICD-10 codes suggested as notes are written
Semantic search
Find all synonyms and related concepts automatically
Unified index
Search across all patient records by any clinical concept
Chart review time
96% reduction
Coding accuracy
+9 points
Query response
Real-time
Records searchable
5x coverage
Why Mixpeek
Mixpeek combines document extraction, NLP classification, and semantic search in a single pipeline. No need to stitch together separate OCR, NER, and search systems. Taxonomy support maps directly to ICD-10 hierarchies, and the retriever handles both keyword and semantic queries across extracted clinical data.
Overview
Clinical NLP transforms unstructured medical text into structured, searchable data. From discharge summaries to pathology reports, Mixpeek extracts clinical entities, classifies them against medical taxonomies, and indexes everything for instant retrieval. Health systems use this to automate coding, power clinical decision support, and enable population health analytics across millions of patient records.
Challenges This Solves
Unstructured Clinical Text
Over 80% of clinical data exists as free-text notes, not structured fields. Physician notes use abbreviations, shorthand, and non-standard formatting that general NLP models cannot parse.
Impact: Critical clinical information is invisible to search and analytics systems, requiring manual chart review at $50-100/hour.
Medical Coding Bottleneck
Assigning ICD-10, CPT, and SNOMED codes to clinical encounters is a manual, error-prone process. Coders review each record individually, leading to backlogs and coding errors that affect reimbursement.
Impact: Average coding turnaround is 3-5 days. Coding errors cost US hospitals $36B annually in denied claims.
Cross-Record Search
Finding all patients with a specific condition, medication, or clinical finding across millions of records requires structured queries — but the data is unstructured.
Impact: Population health queries that should take seconds instead require weeks of manual chart review or custom SQL against incomplete structured data.
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
Text Embedding
Extract semantic embeddings from documents, transcripts and text content
Named Entity Recognition
Identify and extract named entities like people, organizations, and locations
Document Classification
Categorize PDF documents by type, purpose, and content
Keyword Extraction
Identify and extract key phrases and important terms from text
Text Classification
Categorize text into predefined classes or categories
PDF Text Extraction
Extract structured text and layout information from PDFs
Retriever Stages Used
hybrid-knn
Expected Outcomes
94% F1 on medical NER benchmarks
Entity extraction accuracy
94% top-3 accuracy on discharge summaries
ICD-10 coding accuracy
3.2x improvement over keyword-only search
Search recall
10,000+ documents/hour on GPU clusters
Processing throughput
Build Clinical NLP Pipelines
Set up document extraction, medical NER, ICD-10 taxonomy classification, and semantic search across clinical records.
Frequently Asked Questions
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