Role of UMLS in harmonizing SNOMED CT, ICD-10, and other terminologies.
Lost in Translation: The Chaos of Medical Jargon Picture this: A patient walks into
Transform Healthcare Chaos into Clarity with AI-Driven NLP
Automate medical coding, unlock hidden insights from unstructured data, and ensure compliance—faster than ever.
Designed for Healthcare Innovators
THE PROBLEM
Healthcare’s data challenges are costing you time, revenue, and patient outcomes
When clinical details are trapped in PDFs and free-text, teams lose time, revenue, and visibility. Centigrade converts unstructured data into standards-aligned outputs (coding, FHIR/CCD, mappings) to accelerate RCM, analytics, and care
Manual Coding Errors & Delays
Manual ICD-10/CPT coding leads to errors, claim denials, and delayed reimbursements—costing hospitals $262B annually in administrative waste
Unstructured Data Overload
80% of EHR data is unstructured, hiding critical signals like adverse drug reactions (ADRs) in free-text notes, journals, or social media
Slow, Costly FHIR Conversions
Converting PDFs or legacy records to FHIR/CCD standards takes weeks of manual mapping, delaying care coordination and analytics
Non-Compliant Records
PHI leaks in non-deidentified records expose organizations to HIPAA fines (up to $1.5M per violation) and patient distrust
Inaccurate Terminology Mappings
Mismatched mappings (e.g., ICD-10 to symptoms, RxNorm to NDC) create inconsistencies that derail billing, research, and AI models.
Poor-Quality Training Data
Biased, small, or non-compliant datasets lead to unreliable models. 67% of healthcare AI projects fail due to poor training data.
We understand the medical standards
We’re NLP experts who speak healthcare’s language
Our solutions automate workflows, map terminologies, and extract insights so you can focus on what matters: better care, faster revenue cycle, and safer outcomes.
Coding Systems & Terminology
CPT/ICD-10 Coding API: Automate mappings with high accuracy
SNOMED CT & RxNorm Integration: Standardize diagnoses and medications
Medical Abbreviation Resolver: Decipher “SOB” into “Shortness of Breath” instantly
Medical NLP Solutions
Adverse Drug Reaction (ADR) Detection: Scan tweets, journals, and EHRs for safety signals
Medical Record De-Identification: Auto-redact PHI with HIPAA-ready LLMs
Clinical NER: Extract diagnoses, medications, and procedures
Data Extraction & Mapping
PDF-to-FHIR/CCD: Convert unstructured records with 95% accuracy
ICD-10-CM to Symptoms Mapper: Link codes to patient signs automatically
Record Completeness Check: Flag missing data pre-audit
Healthcare Datasets & Training
Synthetic Datasets: Cardiology, oncology, pediatrics, and more
Pre-Annotated EHR Datasets: Jumpstart NLP projects
LLM Training Packs: Fine-tune models for clinical accuracy
How It Works: Simple, Secure, Scalable
Discover – Align on use case, standards (ICD/CPT, FHIR/CCD, SNOMED), and success metrics.
Ingest & Secure – Connect your sources (EHR exports, PDFs, notes) with PHI-safe handling.
Extract & Normalize – Clinical NLP structures the data, maps terminology, and validates outputs.
Integrate & Improve – Deliver via API/files into your workflow; monitor quality and continuously refine.
Blog
Our Latest Articles
How to extract ICD-10 codes from medical records (NLP methods, rule-based systems, manual workflows)
The ICD-10 Code Hunt: A Medical Coder’s Daily Grind Sarah, a medical coder at
Rule-Based Systems vs. Deep Learning in Clinical Text Analysis: A Pragmatic Guide
The ER Chronicles: When “SOB” Isn’t Just a Complaint Picture this: Dr. Lee, an
From Adverse Events to Action: How AI and NLP Are Revolutionizing Drug Safety Monitoring
The Pharmacovigilance Puzzle: A Story of Needles and Haystacks In 2021, a pharmaceutical company
Taming the Chaos: How to Clean Up Noisy Clinical Text for Smarter Healthcare
The EHR Jungle: When Clinical Notes Look Like a Doctor’s Grocery List Imagine a
Teaching AI to Speak Doctor: The Rise of Medical Large Language Models in Healthcare
The EHR Whisperer: When AI Reads Between the Lines Dr. Patel stares at her
Custom-built clinical AI for coding, Interoperability and compliance
Collaborate with our AI research team and speed up your workflow.
SUPPORT
Your questions, answered
What does CENTRIGRADE do ?
We convert unstructured healthcare data (PDFs, clinical notes, scanned docs) into structured, standards-aligned outputs like coding suggestions, FHIR/CCD resources, and terminology mappings.
What types of data can you process?
Clinical notes, discharge summaries, operative notes, lab reports, radiology reports, referral letters, insurance/claims documents, and scanned PDFs.
What standards and code systems do you support?
Commonly ICD-10, CPT, HCPCS, SNOMED CT, LOINC, RxNorm, and FHIR/CCD outputs (based on your workflow needs)
Is this fully automated medical coding?
It can be used as a coding copilot—supporting human coders with suggestions, evidence highlights, and confidence scoring. Final submission can remain human-controlled.
How do you measure accuracy?
We evaluate at code level (precision/recall/F1), chart-level outcomes, and business KPIs like denial reduction, coder throughput, and turnaround time—based on your success criteria
Can you integrate with our EHR/RCM systems?
Yes. We typically integrate via APIs, secure file exchange, or workflow tools—depending on your IT architecture and compliance requirements
Can you customize the solution for our SOPs and payer rules?
Yes. We tailor extraction rules, coding logic, and validation checks to your documentation patterns, specialty, and compliance needs