Skip to content

Feedback: guides-soap-note-generation

Original URL: https://www.assemblyai.com/docs/guides/soap-note-generation
Category: guides
Generated: 05/08/2025, 4:37:39 pm


Generated: 05/08/2025, 4:37:38 pm

Technical Documentation Analysis: SOAP Note Generation Guide

Section titled “Technical Documentation Analysis: SOAP Note Generation Guide”

This documentation provides a functional walkthrough but has several gaps that could frustrate users and limit adoption. The content assumes significant prior knowledge and lacks essential context for successful implementation.

Issue: Unclear what users need before starting Add:

  • Supported audio formats and file size limits
  • Required Python version compatibility
  • Dependencies beyond the SDK
  • Audio quality requirements for medical transcription accuracy
  • Compliance considerations (HIPAA, data handling)

Issue: Oversimplified API key handling Improve:

import os
import assemblyai as aai
# Recommended: Use environment variables for API keys
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
if not aai.settings.api_key:
raise ValueError("Please set your ASSEMBLYAI_API_KEY environment variable")

Issue: No error handling examples provided Add:

try:
transcript = transcriber.transcribe("example.mp3")
if transcript.status == aai.TranscriptStatus.error:
print(f"Transcription failed: {transcript.error}")
return
except Exception as e:
print(f"API request failed: {e}")
return

Current problems:

  • Configuration scattered across multiple code blocks
  • No clear separation between setup and execution
  • Missing workflow overview

Suggested structure:

  1. Quick Start (complete working example)
  2. Prerequisites (requirements, setup, compliance)
  3. Configuration Options (detailed explanations)
  4. Implementation Methods (current two approaches)
  5. Troubleshooting (common issues)
  6. Advanced Usage (customization options)

Replace scattered config with:

def setup_transcription_config(enable_pii_redaction=True):
"""Configure transcription settings for medical audio processing"""
config = aai.TranscriptionConfig()
if enable_pii_redaction:
config.set_redact_pii = True
config.set_redact_pii_policies = [
aai.PIIRedactionPolicy.person_name,
aai.PIIRedactionPolicy.date_of_birth,
# ... other policies
]
return config

Issues:

  • Unclear why some policies are commented out
  • No explanation of medical vs. general PII policies
  • Missing context about compliance implications

Improve with:

# Medical-specific PII policies (commented out as they may be needed for SOAP notes)
# Uncomment only if your use case requires removing medical information:
# aai.PIIRedactionPolicy.medical_condition, # May remove diagnosis info
# aai.PIIRedactionPolicy.medical_process, # May remove procedure details
# aai.PIIRedactionPolicy.drug, # May remove medication names

Add decision matrix:

| Method | Best For | Pros | Cons |
|--------|----------|------|------|
| Summary | Quick, complete notes | Single API call, consistent format | Less control over sections |
| Q&A | Selective regeneration | Granular control, iterative refinement | Multiple API calls, more complex |

Add at the beginning:

import assemblyai as aai
import os
def generate_soap_note(audio_file_path):
"""Complete example: Generate SOAP note from audio file"""
# Setup
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
# Configure transcription
config = aai.TranscriptionConfig(redact_pii=True)
transcriber = aai.Transcriber(config=config)
# Transcribe
print("Transcribing audio...")
transcript = transcriber.transcribe(audio_file_path)
if transcript.status == aai.TranscriptStatus.error:
return f"Error: {transcript.error}"
# Generate SOAP note
print("Generating SOAP note...")
result = transcript.lemur.summarize(
context="Doctor-patient consultation with PII redacted",
answer_format="[SOAP format template]"
)
return result.response
# Usage
soap_note = generate_soap_note("consultation.mp3")
print(soap_note)

Issue: No example outputs shown Add: Sample SOAP notes showing expected format and quality

Add:

def validate_audio_file(file_path):
"""Validate audio file before processing"""
if not os.path.exists(file_path):
raise FileNotFoundError(f"Audio file not found: {file_path}")
file_size = os.path.getsize(file_path)
if file_size > 500 * 1024 * 1024: # 500MB limit example
raise ValueError("File too large. Maximum size: 500MB")
return True

Add:

# Show transcription progress
transcript = transcriber.transcribe(
"example.mp3",
polling_interval=5, # Check status every 5 seconds
timeout=600 # 10 minute timeout
)
print(f"Transcription status: {transcript.status}")

Add section:

## Cost Estimation
- Transcription: $0.37 per hour of audio
- LeMUR processing: $0.003 per input token + $0.024 per output token
- Typical 15-minute consultation: ~$2-5 total cost
Use the [pricing calculator](link) for accurate estimates.
from typing import Optional, List
import assemblyai as aai
def generate_soap_summary(
transcript: aai.Transcript,
custom_format: Optional[str] = None
) -> str:
"""Generate SOAP note using summary method
Args:
transcript: AssemblyAI transcript object
custom_format: Optional custom SOAP format template
Returns:
Generated SOAP note as string
Raises:
APIError: If LeMUR request fails
"""
class SOAPConfig:
"""Configuration for SOAP note generation"""
def __init__(self):
self.pii_redaction = True
self.medical_pii_policies = [...]
self.context_template = "Doctor-patient consultation"
self.soap_format = self._default_soap_format()
def _default_soap_format(self) -> str:
return """[Current SOAP format template]"""
  • Common transcription issues
  • Audio quality problems
  • API rate limits and timeouts
  • LeMUR output formatting problems
  • Audio recording recommendations
  • Privacy and compliance guidelines
  • Quality assurance workflows
  • Integration patterns
  • Healthcare system integration
  • Batch processing workflows
  • Real-time transcription scenarios
  • Processing time estimates
  • Concurrent request limits
  • Optimization strategies for large volumes

This enhanced documentation would significantly improve user success rates and reduce support burden while providing a more professional, production-ready experience.