Automated Medical Records Processing
Extract structured clinical data from unstructured medical records with AI that understands medical terminology, clinical context, and documentation standards
Healthcare organizations process millions of medical records containing valuable clinical information trapped in unstructured formats including physician notes, discharge summaries, imaging reports, and pathology results. Manually extracting and coding this information for quality reporting, prior authorizations, and clinical decision support is labor-intensive and inconsistent.
The Problem
Clinical staff spend hours manually reviewing records to extract diagnoses, medications, test results, and clinical findings for care coordination, quality measures, and utilization review. Unstructured documentation limits data usability for analytics, population health, and clinical decision support while manual abstraction creates bottlenecks and coding inconsistencies.
Unstructured Data Extraction
Critical clinical information is buried in free-text physician notes, imaging reports, and consultation summaries requiring manual reading and abstraction for structured data use.
Manual Chart Abstraction Bottleneck
Nurses and coders spend 30-40% of time on chart abstraction for quality measures, prior authorizations, and appeals, delaying care and driving administrative costs.
Coding Accuracy & Consistency
Manual coding and abstraction quality varies by reviewer expertise and volume pressure, leading to missed diagnoses, incorrect severity coding, and incomplete documentation.
How OpenClaw Solves This
OpenClaw's medical records processing AI analyzes unstructured clinical documentation to extract diagnoses, medications, procedures, lab values, and clinical findings automatically. The system understands medical terminology, contextual negation, temporal relationships, and clinical significance to produce accurate structured data for coding, reporting, and clinical workflows.
Clinical NLP & Entity Extraction
Extracts medical entities including diagnoses, medications, lab results, vitals, procedures, and anatomical references from physician notes, reports, and discharge summaries.
Contextual Understanding & Negation Detection
Understands clinical context including negation ("no evidence of diabetes"), historical conditions, family history, and differential diagnoses to extract accurate, current problems.
Automated Medical Coding Suggestions
Generates ICD-10, CPT, and HCPCS code suggestions from clinical documentation with supporting evidence and confidence scores for coder review and validation.
Longitudinal Record Integration
Integrates data across multiple encounters, identifies chronic conditions, tracks medication changes, and maintains patient-level clinical timelines for comprehensive views.
How Medical Records Processing Works
Document Ingestion & Classification
System ingests medical documents including notes, reports, and summaries, classifying by type (progress note, discharge summary, pathology report) for targeted extraction.
Clinical Entity Extraction
AI analyzes text to extract diagnoses, medications, procedures, lab values, vital signs, and clinical findings with associated metadata including dates, providers, and confidence.
Coding & Abstraction
System generates structured outputs including medical codes, quality measure numerators, risk adjustment HCCs, and abstracted data fields with source documentation references.
Review & Integration
Extracted data and coding suggestions are queued for clinical reviewer validation, then integrated into EHR structured fields, quality reporting systems, or coding workflows.
Measurable Results
Faster Chart Abstraction
Reduce chart review and abstraction time from hours to minutes by starting with AI-extracted structured data requiring only validation.
Coding Accuracy
Improve coding completeness and accuracy by identifying diagnoses and procedures missed in manual review, capturing proper severity and specificity.
Data Utilization
Unlock clinical insights from unstructured documentation for analytics, population health, clinical decision support, and quality improvement initiatives.
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Frequently Asked Questions
Automate Your Medical Records Processing
Stop spending hours on manual chart abstraction. Extract structured clinical data automatically with AI that understands medical documentation.
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