Medical Scribe Dictation

• openai or anthropic - LLM for structure extraction
• spacy + scispacy - Medical NLP processing
• faster-whisper (optional) - Local STT
• pydantic - Data validation

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/medical-scribe-dictation"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py -h
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Speech-to-Text Processing: Transcribe audio or process pre-transcribed text
• SOAP Structure Generation: Auto-organize clinical content into standard sections
• Medical Terminology Handling: Normalize abbreviations, expand acronyms, verify drug names
• Clinical Quality Checks: Flag missing required elements, suggest clarifications
• Multi-Specialty Support: Adaptable templates for internal medicine, surgery, pediatrics, etc.

Usage

Processing Pre-Transcribed Text

python scripts/main.py --input "patient presents with..." --output-format soap

Processing Audio File (requires whisper/faster-whisper)

python scripts/main.py --audio consultation.wav --output note.md

Python API

from scripts.main import MedicalScribe

scribe = MedicalScribe(specialty="internal_medicine")
soap_note = scribe.process_dictation(transcription_text)
print(soap_note.to_markdown())

Parameters

SOAP Output Structure

# Clinical Note - [Date]

## Subjective
Chief Complaint:
History of Present Illness:
Review of Systems:
Past Medical History:
Medications:
Allergies:
Social History:
Family History:

## Objective
Vital Signs:
Physical Examination:
Diagnostic Studies:

## Assessment
Primary Diagnosis:
Differential Diagnoses:
Clinical Reasoning:

## Plan
Diagnostic:
Therapeutic:
Patient Education:
Follow-up:

Technical Architecture

Components

1. Transcription Module (optional): Whisper-based STT with medical vocabulary fine-tuning
2. Segmentation Engine: NLP-based section identification and content classification
3. Terminology Processor: Medical NER (Named Entity Recognition) and normalization
4. SOAP Assembler: Structured output generation with specialty-specific formatting
5. Quality Validator: Completeness checks and clinical red-flag detection

Technical Difficulty

High - Requires medical domain expertise, complex NLP pipelines, and clinical validation.

Known Limitations

• Medical terminology accuracy depends on speech clarity
• Ambiguous dictation may require human clarification
• Drug name verification recommended before finalizing
• Does not replace physician review for critical cases

References

See references/ for:

• soap-templates.md - Specialty-specific SOAP templates
• medical-abbreviations.json - Common abbreviation mappings
• terminology-sources.md - Medical ontology references (SNOMED CT, ICD-10)
• example-cases.md - Sample dictations with expected outputs

Safety Notes

⚠️ Clinical Validation Required: All generated notes must be reviewed by the attending physician before entering the medical record.

⚠️ No Diagnostic Authority: This tool structures clinical information but does not provide diagnostic suggestions.

Risk Assessment

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

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of medical-scribe-dictation and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

medical-scribe-dictation only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.