automated-soap-note-generator

What this skill does

# 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
- Critical care situations requiring real-time precise documentation
- Cases requiring differential diagnosis prioritization without physician input

**⚠️ ALWAYS Required:**
- Physician review and approval before entering into patient record
- Verification of medical facts and clinical accuracy
- Confirmation of medication names, dosages, and instructions

## Integration with Other Skills

**Upstream Skills:**
- `medical-scribe-dictation`: Convert physician verbal dictation to text input
- `ehr-semantic-compressor`: Summarize lengthy EHR notes for SOAP generation
- `dicom-anonymizer`: Prepare imaging reports for SOAP inclusion
- `audio-script-writer`: Convert audio recordings to text format

**Downstream Skills:**
- `medical-email-polisher`: Professional communication of SOAP summaries to patients
- `clinical-data-cleaner`: Standardize extracted data for research databases
- `hipaa-compliance-auditor`: Verify de-identification before sharing documentation
- `discharge-summary-writer`: Generate discharge summaries from SOAP encounters
- `referral-letter-generator`: Create referral letters based on Assessment and Plan sections

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

## Core Capabilities

### 1. Input Processing and Preprocessing

Handle various input formats and prepare for NLP analysis:

```python
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"
)
```

**Input Preprocessing Steps:**
1. **Text Cleaning**: Remove filler words ("um", "uh"), timestamps, speaker labels
2. **Sentence Segmentation**: Split into clinically meaningful segments
3. **Normalization**: Standardize abbreviations and medical shorthand
4. **Encoding Detection**: Handle various file formats (UTF-8, ASCII, etc.)

**Parameters:**
| 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 |

*Either `input_text` or `transcript_path` required

**Best Practices:**
- Always verify input text quality (clear audio → better transcription → better SOAP)
- Remove patient identifiers before processing unless in secure environment
- Split long encounters (>30 minutes) into logical segments
- Flag ambiguous abbreviations for manual review

### 2. Medical Named Entity Recognition (NER)

Identify and extract medical concepts from unstructured text:

```python
# 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"}
#   ]
# }
```

**Entity Types Recognized:**
| 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 |

**Common Issues and Solutions:**

**Issue: Missed medications**
- Symptom: Generic names not recognized (e.g., "water pill" for diuretic)
- Solution: Manual review required; tool flags colloquial terms for verification

**Issue: Ambiguous abbreviations**
- Symptom: "SOB" could be shortness of breath or something else
- Solution: Context-aware disambiguation; flag uncertain cases

**Issue: Misspelled drug names**
- Symptom: "metfomin" instead of "metformin"
- Solution: Fuzzy matching with confidence threshold; flag low-confidence matches

### 3. SOAP Section Classification

Automatically categorize sentences into appropriate SOAP sections:

```python
# 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"]
# }
```

**Classification Rules:**
| 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 |

**Multi-label Handling:**
Some sent