automated-soap-note-generator

LeoYeAI/openclaw-master-skillsBiology Medicine and BioinformaticsAudit pendingSnapshot bef3f0086ba8

Summary

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SKILL.md

Automated SOAP Note Generator

Overview

AI-powered clinical documentation tool that converts unstructured clinical input into professionally formatted SOAP notes compliant with medical documentation standards.

Key Capabilities:

Intelligent Parsing: Extracts structured information from free-text clinical narratives
SOAP Classification: Automatically categorizes content into Subjective, Objective, Assessment, Plan sections
Medical Entity Recognition: Identifies symptoms, diagnoses, medications, procedures, and anatomical locations
Temporal Analysis: Extracts timeline information (onset, duration, progression)
Template Generation: Produces standardized SOAP format suitable for EHR integration
Multi-modal Input: Accepts text dictation, transcripts, or clinical notes

When to Use

✅ Use this skill when:

Converting physician dictation into structured SOAP format for efficiency
Processing audio-to-text transcripts from patient encounters
Transforming consultation rough notes into formal documentation
Generating initial draft documentation to reduce administrative burden
Standardizing clinical encounter summaries for consistency
Creating preliminary notes for routine follow-up visits

❌ Do NOT use when:

Input contains PHI that hasn't been de-identified for testing/training
Complex psychiatric cases requiring nuanced mental status documentation → Use specialized psychiatric documentation tools
Surgical procedures requiring operative report detail → Use operative-report-generator
Patient requires nuanced clinical reasoning beyond text extraction
Legal or forensic documentation requiring exact transcription → Use verbatim transcription services

Medical Scribe Dictation (audio→text) → 
  Automated SOAP Note Generator (this skill) → 
    Physician Review → 
      EHR Entry / 
      Medical Email Polisher (patient communication) / 
      Referral Letter Generator (referrals)

from scripts.soap_generator import SOAPNoteGenerator

generator = SOAPNoteGenerator()

# Process text input
soap_note = generator.generate(
    input_text="Patient presents with 2-day history of chest pain, radiating to left arm...",
    patient_id="P12345",
    encounter_date="2026-01-15",
    provider="Dr. Smith"
)

# Process from audio transcript
soap_note = generator.generate_from_transcript(
    transcript_path="consultation_transcript.txt",
    patient_id="P12345"
)

Parameter	Type	Required	Description	Default
`input_text`	str	Yes*	Raw clinical text or dictation	None
`transcript_path`	str	Yes*	Path to transcript file	None
`patient_id`	str	No	Patient identifier (MUST be de-identified for testing)	None
`encounter_date`	str	No	Date in ISO 8601 format (YYYY-MM-DD)	Current date
`provider`	str	No	Healthcare provider name	None
`specialty`	str	No	Medical specialty context	"general"
`verbose`	bool	No	Include confidence scores	False

# Extract entities with context
entities = generator.extract_medical_entities(
    "Patient has history of hypertension and diabetes, 
     currently taking lisinopril 10mg daily and metformin 500mg BID"
)

# Returns structured entities:
# {
#   "diagnoses": ["hypertension", "diabetes mellitus"],
#   "medications": [
#     {"name": "lisinopril", "dose": "10mg", "frequency": "daily"},
#     {"name": "metformin", "dose": "500mg", "frequency": "BID"}
#   ]
# }

Category	Examples	Notes
Diagnoses	diabetes, hypertension, pneumonia	ICD-10 compatible where possible
Symptoms	chest pain, headache, nausea	Includes severity modifiers
Medications	metformin, lisinopril, aspirin	Extracts dose, route, frequency
Procedures	ECG, CT scan, blood draw	Includes body site
Anatomy	left arm, chest, abdomen	Laterality and location
Lab Values	glucose 120, BP 140/90	Units and reference ranges
Temporal	yesterday, 3 days ago, chronic	Normalized to relative dates

# Classify content into SOAP sections
classified = generator.classify_soap_sections(
    "Patient reports chest pain for 2 days. Physical exam shows BP 140/90. 
     Likely angina. Schedule stress test and start aspirin 81mg daily."
)

# Output structure:
# {
#   "Subjective": ["Patient reports chest pain for 2 days"],
#   "Objective": ["Physical exam shows BP 140/90"],
#   "Assessment": ["Likely angina"],
#   "Plan": ["Schedule stress test", "start aspirin 81mg daily"]
# }

Section	Content Type	Examples
S - Subjective	Patient-reported information	"Patient states...", "Patient reports...", "Complains of..."
O - Objective	Observable/measurable findings	Vital signs, physical exam, lab results, imaging
A - Assessment	Clinical interpretation	Diagnosis, differential, clinical impression
P - Plan	Actions to be taken	Medications, procedures, follow-up, patient education

# Extract temporal relationships
timeline = generator.extract_temporal_info(
    "Patient had chest pain starting 3 days ago, worsening since yesterday. 
     Had similar episode 2 months ago that resolved with rest."
)

# Returns:
# {
#   "onset": "3 days ago",
#   "progression": "worsening",
#   "previous_episodes": [
#     {"time": "2 months ago", "resolution": "with rest"}
#   ]
# }

# Detect negations and uncertainties
analysis = generator.analyze_certainty(
    "Patient denies chest pain. No shortness of breath. 
     Possibly had fever yesterday but not sure."
)

# Identifies:
# - "denies chest pain" → Negative finding (important!)
# - "No shortness of breath" → Negative finding
# - "Possibly had fever" → Uncertain finding (flag for verification)

Type	Cues	Action
Negation	denies, no, without, absent	Mark as negative finding
Uncertainty	possibly, maybe, uncertain, ?	Flag for physician review
Hypothetical	if, would, could	Note as conditional
Family History	family history of, mother had	Separate from patient findings

# Generate complete SOAP note
soap_output = generator.generate_soap_document(
    structured_data=classified,
    format="markdown",  # Options: markdown, json, hl7, text
    include_metadata=True
)

# SOAP Note

**Patient ID:** P12345  
**Date:** 2026-01-15  
**Provider:** Dr. Smith

## Subjective
Patient reports [extracted symptoms with duration]. History of [chronic conditions]. 
Currently taking [medications]. Patient denies [negative findings].

## Objective
**Vital Signs:** [BP, HR, RR, Temp, O2Sat]  
**Physical Examination:** [Exam findings by system]  
**Laboratory/Data:** [Relevant results]

## Assessment
[Primary diagnosis/differential]  
[Clinical reasoning summary]

## Plan
1. [Action item 1]
2. [Action item 2]
3. [Follow-up instructions]

---
*Generated by AI. REQUIRES PHYSICIAN REVIEW before entry into patient record.*

Format	Use Case	Notes
Markdown	Human review, documentation	Default, readable
JSON	System integration, research	Structured data
HL7 FHIR	EHR integration	Healthcare standard
Plain Text	Simple documentation	Minimal formatting
CSV	Data analysis, research	Tabular data export

# Step 1: Process audio to text (using medical-scribe-dictation or external)
# Assuming you have transcript: consultation.txt

# Step 2: Generate SOAP note
python scripts/main.py \
  --input-file consultation.txt \
  --patient-id P12345 \
  --provider "Dr. Smith" \
  --specialty "cardiology" \
  --output soap_draft.md \
  --format markdown

# Step 3: Review output
# - Open soap_draft.md
# - Verify medical accuracy
# - Correct any errors
# - Add missing clinical reasoning

# Step 4: Finalize (after physician approval)
# - Copy approved content to EHR
# - Or use for patient communication

from scripts.soap_generator import SOAPNoteGenerator
from scripts.post_processor import ReviewFormatter

# Initialize
generator = SOAPNoteGenerator()
reviewer = ReviewFormatter()

# Generate draft
with open("dictation.txt", "r") as f:
    raw_text = f.read()

draft = generator.generate(
    input_text=raw_text,
    patient_id="P12345",
    encounter_date="2026-01-15",
    provider="Dr. Smith",
    specialty="internal_medicine"
)

# Add physician review markers
marked_draft = reviewer.add_review_markers(draft)

# Save with warning header
reviewer.save_with_disclaimer(
    marked_draft, 
    output_path="soap_draft_review.md",
    disclaimer="REQUIRES PHYSICIAN REVIEW - NOT FOR DIRECT ENTRY"
)

output/
├── soap_draft.md              # Generated SOAP note
├── entities_extracted.json     # Structured medical entities
├── classification_report.txt   # Confidence scores for each section
└── review_checklist.md         # Items requiring manual verification

Parameter	Type	Default	Required	Description
`--input`, `-i`	string	-	No	Input clinical text directly
`--input-file`, `-f`	string	-	No	Path to input text file
`--output`, `-o`	string	-	No	Output file path
`--patient-id`, `-p`	string	-	No	Patient identifier
`--provider`	string	-	No	Healthcare provider name
`--format`	string	markdown	No	Output format (markdown, json)

# Generate SOAP from text
python scripts/main.py --input "Patient reports chest pain..." --output note.md

# From file
python scripts/main.py --input-file consultation.txt --patient-id P12345 --provider "Dr. Smith"

# JSON output
python scripts/main.py --input-file notes.txt --format json --output note.json

Risk Indicator	Assessment	Level
Code Execution	Python script executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Low
Data Exposure	May process PHI (Protected Health Information)	High
HIPAA Compliance	Must be used in compliant environment	High

# Python 3.7+
# No external packages required (uses standard library)

automated-soap-note-generator

Summary

SKILL.md

Automated SOAP Note Generator

Overview

When to Use

Integration with Other Skills

Core Capabilities

1. Input Processing and Preprocessing

2. Medical Named Entity Recognition (NER)

3. SOAP Section Classification

4. Temporal Information Extraction

5. Negation and Uncertainty Detection

6. Structured SOAP Generation

Complete Workflow Example

Quality Checklist

Common Pitfalls

Troubleshooting

References

Scripts

Performance and Resources

Limitations

Regulatory and Legal Notes

Version History

Parameters

Usage

Basic Usage

Risk Assessment

Security Checklist

Prerequisites

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

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