Rule-Based Safety Supervisor for Multimodal Opioid-Free Anesthesia
CRITICAL WARNING: This is a computational research prototype developed for educational purposes. It is NOT validated for clinical use and must NEVER be used in patient care or integrated with medical devices.
This is not:
- A medical device or clinical decision support system
- Approved by FDA or any regulatory authority
- Validated for patient safety or clinical accuracy
- Intended for integration with anesthesia equipment
- A substitute for clinical judgment or monitoring
This is:
- Independent pre-medical computational research project
- Educational demonstration of safety supervision concepts
- Proof-of-concept for algorithm design discussion
- Medical school application portfolio material
Regulatory Status:
This software has NOT undergone clinical validation, regulatory review, or safety testing. It is NOT approved for clinical deployment under any circumstances.
Institutional Affiliation:
This is an independent educational project. It is not affiliated with, endorsed by, or approved by University of Washington, UW Medicine, or any clinical institution.
Clinical Use Warning:
Use of this software in any patient care environment would constitute unauthorized practice of medicine and violate regulatory requirements. Clinical anesthesia requires trained professionals, approved monitoring equipment, and validated decision support systems.
Liability:
This work is provided "as is" without warranty of any kind. The author assumes no liability for any use or misuse of this software.
Author Status:
Pre-medical student. Not a licensed healthcare professional. Not engaged in clinical practice.
Smart NOA Controller is a deterministic, rule-based software prototype designed to simulate closed-loop safety supervision concepts for multimodal opioid-free anesthesia protocols. This repository presents an educational proof-of-concept examining algorithmic safety interlock design.
Purpose:
Demonstrate computational thinking about anesthesia safety systems through simulation and algorithm development for educational portfolio purposes.
Scope:
Educational exploration of how rule-based systems might theoretically support clinical decision-making. Not a functional clinical system.
Multimodal anesthesia approaches aim to reduce perioperative opioid requirements and improve Enhanced Recovery After Surgery (ERAS) outcomes. Implementation introduces operational complexity:
- Concurrent administration of multiple anesthetic agents
- Drug-specific contraindications and interaction profiles
- Dynamic vital-sign monitoring requirements
- Real-time safety assessment needs
- Increased cognitive load for anesthesia providers
This project simulates a safety-oriented, rule-driven decision framework to explore computational approaches to:
- Enforcing evidence-based dosing limit concepts
- Continuous contraindication surveillance simulation
- Automated alert generation based on safety rule violations
- Hemodynamic threshold monitoring models
Important: This is a theoretical exploration of safety concepts, not a functional clinical system.
Rule-Based Approach:
- Deterministic logic (no machine learning or adaptive algorithms)
- Transparent decision pathways
- Explicit contraindication checking
- Conservative safety thresholds
Educational Focus:
- Demonstrates understanding of clinical decision logic
- Shows systems thinking and computational problem-solving
- Illustrates safety-critical software design concepts
- Not intended as actual clinical implementation
Smart NOA Controller Architecture (Educational Simulation)
├── Patient State Monitor (simulated vital signs input)
├── Drug Administration Tracker (simulated infusion data)
├── Contraindication Engine (rule-based checking)
├── Safety Interlock System (alert generation)
└── Logging and Audit Trail (event recording)
Note: All components are simulated for educational demonstration. No actual medical device integration exists or is intended.
Simulated monitoring rules:
- Heart rate <50 or >120 bpm → Alert
- Systolic BP <90 or >180 mmHg → Alert
- SpO₂ <92% → Alert
- Sustained parameter violations → Simulated infusion hold recommendation
Educational note: Actual clinical thresholds are patient-specific and require attending physician judgment.
Simulated contraindication checking:
- Dexmedetomidine: Check for bradycardia, AV block
- Ketamine: Check for hypertension, psychosis history
- Magnesium: Check for renal function, neuromuscular blockade
- Lidocaine: Check for cardiac conduction abnormalities
Educational note: Real clinical implementation requires comprehensive drug databases and patient-specific risk assessment.
- Language: Python 3.8+
- Purpose: Educational simulation and algorithm demonstration
- Dependencies: Standard Python libraries (no medical device interfaces)
git clone https://github.com/collingeorge/Smart-NOA-Controller.git
cd Smart-NOA-Controller
pip install -r requirements.txtWARNING: This installation is for code review and educational purposes only. Do not attempt to connect to any medical devices or use in clinical settings.
# Educational simulation example
python simulate_safety_checks.py --scenario example_case.jsonNote: All scenarios are fictional and generated for educational demonstration.
- A medical device or clinical decision support system
- Validated for clinical accuracy or patient safety
- Approved by any regulatory authority (FDA, etc.)
- Suitable for clinical implementation without extensive validation
- A replacement for clinical judgment or monitoring equipment
- Educational exploration of safety system concepts
- Demonstration of computational thinking in healthcare
- Portfolio piece showing systems design understanding
- Proof-of-concept for academic discussion
- Pre-medical research project
If this concept were ever to be developed into a clinical system (not the intent of this project), it would require:
-
Regulatory Approval:
- FDA 510(k) clearance or PMA approval
- CE marking (Europe)
- Compliance with IEC 60601 medical device standards
-
Clinical Validation:
- Prospective clinical trials
- Safety and efficacy demonstration
- IRB oversight and informed consent
-
Technical Requirements:
- Medical-grade hardware certification
- Cybersecurity validation (FDA guidance)
- Interoperability standards (HL7, FHIR)
- Redundant safety systems
-
Institutional Approval:
- Hospital review and governance approval
- Risk management assessment
- Clinician training programs
- Ongoing quality monitoring
Current status: This project has NONE of the above. It is educational simulation only.
For continued learning:
- Explore machine learning approaches to anesthesia monitoring (simulation only)
- Study existing FDA-approved clinical decision support systems
- Investigate medical device software development standards
- Research human factors in clinical alarm design
Not planned:
- Clinical deployment
- Medical device development
- Commercial development
This educational project was informed by:
- Multimodal anesthesia literature (ERAS guidelines, ASRA recommendations)
- Clinical decision support system design principles
- Medical device software standards (IEC 62304 for educational reference)
- Anesthesia safety and monitoring literature
Complete references available in references/ directory.
Author: Collin B. George, BS
Project Type: Independent pre-medical computational research
Status: Preparing for medical school matriculation 2026
Educational Context: Computational exploration of anesthesia safety concepts
GitHub: github.com/collingeorge
ORCID: 0009-0007-8162-6839
License: MIT
The author is grateful to educators and mentors who supported independent learning in computational health sciences and anesthesia safety concepts.
This project represents independent educational exploration and does not constitute collaboration with any clinical institution or medical device company.
This is an educational project. Feedback welcome for:
- Algorithm design improvements (educational discussion)
- Code quality and software engineering practices
- Additional safety rule frameworks (theoretical)
- Educational use in teaching environments
Not seeking:
- Clinical implementation partners
- Medical device development collaboration
- Commercial applications
Issues and Pull Requests: Welcome for educational improvements only.
If you reference this work in presentations or academic writing:
George CB. Smart NOA Controller: Rule-Based Safety Supervisor for
Multimodal Opioid-Free Anesthesia (Educational Prototype). GitHub
Repository. Published December 2025. Available from:
https://github.com/collingeorge/Smart-NOA-Controller
[Accessed: date]
This project is licensed under the MIT License - see the LICENSE file for details.
MIT License Summary:
- Free to use, modify, and distribute for educational purposes
- Provided "as is" without warranty
- Author not liable for any use or misuse
- Must include original copyright notice
Additional Terms for This Project:
- Use for educational and research purposes only
- Absolutely prohibited for clinical use
- No medical device integration permitted
- Requires explicit disclaimer if code is adapted
© 2025 Collin B. George — Licensed under MIT License
This software is an educational prototype demonstrating computational concepts in anesthesia safety supervision.
It is NOT:
- Clinically validated
- Regulatory approved
- Safe for patient care
- Intended for medical device integration
Any clinical use would be:
- Unauthorized practice of medicine
- Violation of medical device regulations
- Dangerous to patient safety
- Illegal in most jurisdictions
For educational discussion and portfolio demonstration only.