Smart NOA: A Rule-Checking Simulation and Its Limitations
Educational work by a pre-medical student — not medical advice. Not a clinical protocol, prescription, or patient handout, and not for patient care. See DISCLAIMER.
Collin B. George, pre-medical student | Independent educational portfolio
Review draft - 27 October 2026
Educational scope. This is an independent educational discussion, not clinical guidance, a clinical trial, a validated decision aid, or an institutional publication. No institutional endorsement is claimed.
Abstract
This project demonstrates a small configurable rule-checking simulation in Python. Fictional profiles and randomly generated vital signs drive updates to in-memory values. A central-compartment model with an effect-site state illustrates numerical concentration updates; it is not a three-compartment model or a target-controlled infusion implementation. No performance in patients, prevention of adverse events, or clinical dosing accuracy is established.
Implementation
The program reads example parameters from a YAML file. Selected profile fields trigger a small set of configured lockouts. These checks are incomplete: absence of a configured lockout does not establish clinical acceptability. Unrecognized drug names return an unassessed result rather than a safety assurance.
The concentration model uses a central volume V, elimination constant k10, and effect-site equilibration constant ke. The educational update is
Cp(next) = max(0, Cp + (R/V - k10*Cp)*dt)
Ce(next) = max(0, Ce + ke*(Cp(next) - Ce)*dt)
With input R in micrograms/minute and volume in litres, concentration is micrograms/litre, numerically equivalent to ng/mL. The implementation rejects invalid numerical inputs and time steps exceeding its stated explicit-Euler bound. That numerical bound does not establish pharmacologic validity or simulation accuracy.
The model has one central compartment and an effect-site state. It lacks peripheral distribution compartments and is not a validated population model. Its parameters and example rates are not clinical recommendations.
State transitions
Configured low-heart-rate or low-mean-pressure events set selected simulated values to zero and latch a hold. A subsequent acceptable reading does not automatically restart values. Low-heart-rate checking is not conditioned on a model-derived effect-site threshold. These are demonstrations of state logic, not a rescue protocol.
Verification and limitations
44 software tests exercise sample lockouts, fail-safe handling of unusable vital-sign values, in-loop enforcement of lockouts and latched holds, simulation rate ceilings, case-insensitive rule matching, numerical updates, unknown-drug handling, invalid inputs, simultaneous alerts, overflow rejection without partial state mutation, unit-conversion boundaries, and latched holds. Passing tests is not clinical validation. No clinical event-prevention rate is reported. Random vital signs are independent of drug concentrations: the model does not simulate a physiologic feedback response.
The program lacks comprehensive input validation, drug interaction modeling, monitoring of actual signals, sensor-failure handling, human-factors evaluation, and a validated alarm architecture. There are no medical-device interfaces. Do not enter real patient information or connect equipment.
Evidence and regulatory boundary
Avoiding opioids does not alone establish safer anesthesia. The POFA randomized trial reported important adverse outcomes with its dexmedetomidine-based regimen [pofa]. This simulation does not test either that regimen or an alternative clinical strategy.
Regulatory status depends on intended use and applicable criteria. An educational disclaimer does not determine whether future clinical functionality is a device or specify a clearance pathway [fda].
License
Code is licensed under MIT. Intended-use warnings do not add field-of-use restrictions to MIT or guarantee immunity from liability. Third-party materials retain their own rights.
References
[pofa] Beloeil H et al. Balanced opioid-free anesthesia with dexmedetomidine versus balanced anesthesia with remifentanil for major or intermediate noncardiac surgery. Anesthesiology. 2021;134:541-551. https://pubs.asahq.org/anesthesiology/article-pdf/134/4/541/512443/20210400.0-00011.pdf
[fda] US Food and Drug Administration. Clinical Decision Support Software. January 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software