Icd10 Cpt Coding Assistant

Automatically recommend ICD-10 diagnosis codes and CPT procedure codes from clinical notes. Trigger when: user provides clinical notes, patient encounter summaries, discharge summaries, or asks for medical coding assistance. Use for healthcare providers, medical coders, and billing professionals who need accurate code recommendations.

A medical coding assistant that parses clinical notes and recommends appropriate ICD-10 diagnosis codes and CPT procedure codes with confidence scoring.

Overview

This skill analyzes clinical documentation to extract relevant medical information and map it to standardized coding systems:

• ICD-10-CM: International Classification of Diseases, 10th Revision, Clinical Modification (diagnosis codes)
• CPT: Current Procedural Terminology (procedure/service codes)

Technical Difficulty: HIGH ⚠️

⚠️ HUMAN REVIEW REQUIRED: Medical coding directly impacts billing, reimbursement, and clinical documentation. All recommendations must be verified by a certified medical coder or healthcare provider.

Usage

python scripts/main.py --input "clinical_note.txt" [--format json|text]

Or use programmatically:

from scripts.main import CodingAssistant

assistant = CodingAssistant()
result = assistant.analyze("Patient presents with acute bronchitis...")
print(result.icd10_codes)
print(result.cpt_codes)

Parameters

Parameter	Type	Default	Required	Description
--input, -i	string	-	Yes	Path to clinical note file
--format, -f	string	json	No	Output format (json, text)
--output, -o	string	stdout	No	Output file path
--confidence-threshold	float	0.7	No	Minimum confidence score (0.0-1.0)
--include-alternatives	flag	false	No	Include alternative code suggestions

Input Format

Accepts clinical notes in various formats:

• Free-text narrative
• SOAP notes (Subjective, Objective, Assessment, Plan)
• Discharge summaries
• Progress notes
• Procedure reports

Output Format

ICD-10 Recommendations

{
  "icd10_codes": [
    {
      "code": "J20.9",
      "description": "Acute bronchitis, unspecified",
      "confidence": 0.92,
      "evidence": ["cough for 5 days", "wheezing on exam"],
      "alternatives": ["J20.0", "J44.9"]
    }
  ]
}

CPT Recommendations

{
  "cpt_codes": [
    {
      "code": "99213",
      "description": "Office visit, established patient, moderate complexity",
      "confidence": 0.85,
      "evidence": ["detailed history", "low complexity decision making"],
      "time": "20 minutes"
    }
  ]
}

Confidence Scoring

• 0.90-1.00: High confidence - Clear documentation, unambiguous mapping
• 0.70-0.89: Medium confidence - Good documentation, some interpretation required
• 0.50-0.69: Low confidence - Incomplete documentation, multiple possibilities
• <0.50: Very low confidence - Insufficient information, manual review essential

Limitations

1. No Medical Advice: This tool does not provide clinical advice or diagnoses
2. Coding Complexity: Cannot handle all coding nuances (comorbidities, sequencing, modifiers)
3. Regional Variations: May not account for payer-specific coding requirements
4. Updates: Code sets may not reflect the latest annual updates

References

See references/ folder for:

• icd10_common_codes.json: Frequently used ICD-10 codes by specialty
• cpt_common_codes.json: Frequently used CPT codes by specialty
• coding_guidelines.md: General coding guidelines and conventions

Safety & Compliance

• HIPAA Awareness: Ensure de-identification of PHI before processing
• Audit Trail: Maintain records of automated recommendations for compliance
• Human Oversight: All codes must be reviewed and approved by qualified personnel

Dependencies

• Python 3.8+
• See requirements.txt for package dependencies

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support