Clinical Document Intelligence & Chart Abstraction
Automate the extraction of structured clinical data from unstructured documents — progress notes, discharge summaries, operative reports — at scale and with clinical-grade accuracy.
Turning Clinical Documents Into Structured Data
The majority of clinically valuable information in a patient record is locked inside unstructured text — progress notes, discharge summaries, consultation reports, and operative notes. Data2Chart uses advanced NLP and clinical AI to extract, structure, and code this information automatically.
Built on the Data2iQ platform, Data2Chart integrates with any EHR or document management system and delivers structured clinical data to analytics, quality measurement, and research programs — without manual chart abstraction.
The Challenge
Clinical documentation is rich with information that drives quality measurement, risk adjustment, research, and population health — but extracting it requires manual chart abstraction that is slow, expensive, and inconsistent. At scale, manual abstraction is simply not feasible, leaving critical clinical data unused.
The Data2Chart Solution
Data2Chart automates chart abstraction using clinical NLP models trained on healthcare-specific language. The system extracts diagnoses, procedures, medications, vitals, and clinical findings from free-text documents — mapping them to standard terminologies and delivering structured data to downstream systems.
What You Gain
Clinical NLP Extraction
Healthcare-specific NLP models extract diagnoses, procedures, medications, allergies, vitals, and clinical findings from any document type — with accuracy validated against manual abstraction benchmarks.
Automated Coding
Extracted clinical concepts are automatically mapped to ICD-10, CPT, SNOMED CT, and RxNorm — reducing coding burden and improving coding accuracy and completeness.
Quality Measure Support
Pre-built extractors for HEDIS, CMS quality measures, and Joint Commission standards — automating the data collection that drives quality reporting and accreditation.
Research-Ready Datasets
De-identified, structured clinical datasets for research, registry submission, and population health analytics — produced at scale without manual abstraction effort.
What It Does
Ingest
Clinical documents are ingested from EHR systems, document management platforms, and scanned records via HL7, FHIR, and direct integration — in real time or batch.
Parse
NLP models identify document type, section structure, and clinical entities — extracting relevant information with context-aware precision.
Code
Extracted clinical concepts are mapped to standard terminologies (ICD-10, CPT, SNOMED CT, RxNorm) and validated against clinical rules and coding guidelines.
Deliver
Structured, coded clinical data is delivered to quality programs, analytics platforms, research registries, and downstream EHR systems via API or direct feed.
Standards & Security
See Data2Chart™ in Action
Request a personalized demo and see how Data2Chart™ can transform your data workflows.