Proof of Concept | danielpettus.com
AI That Acts. Not Just Alerts.
AI Agents in Acute Care
Autonomous Therapy. Continuous Learning.
Vendor-agnostic. The platform acute care has been waiting 25 years for.
A post-CABG patient in the ICU requires continuous titration of six high-risk infusions. Reactive AI agents watch vital signs and labs in real time, surfacing nurse-confirmed titration recommendations. Simultaneously, a cloud-based deep learning engine continuously analyzes the patient’s full clinical trajectory, integrating outcomes data, pharmacogenomics, and population models, to proactively optimize the therapy regimen. When the AI recommends a medication change, it automatically routes an updated order to pharmacy for approval, updates the automated dispensing cabinet, and alerts nursing, all before the nurse would have noticed the drift. The nurse remains in control. The AI never stops learning.
● 6 Reactive Agents
▲ Deep Learning Engine Active
IHE PCD · HL7 v2.6 · FHIR R4
Post-CABG ×3 · ICU Day 1
ICU Bed 4 | Post-CABG LIVE
LEVO 4mcg/mL 0.08 mcg/kg/min INFUSION HR:82 MAP:72 SpO2:97 AI ENGINE 6 AGENTS + DEEP LEARNING
ECG  ·  CONTINUOUS  ·  LEAD II
🧠
Deep Learning Optimization Engine
Proactive therapy adjustment: pharmacy, dispensing cabinet, and nursing notification
☁ Cloud  ·  Analyzing
🧠
AI Engine
Deep learning
outcomes model
Monitoring
📋
CPOE
Updated med order
AI recommendation
Pending
💊
Pharmacy
Pharmacist review
and approval
Pending
Dispensing Cabinet
Formulary updated
automatically
Pending
👩‍⚕️
Nursing
Pump auto-programmed
Nurse notified handheld
Pending
Patient
Optimized therapy
best possible outcome
Goal
🏠
Discharge & Home
Wearables connected
Cardiologist linked
AI continues at home
Connected
Live Demo
Real Cloud AI
in Action
🧠 Deep Learning Insight
Continuous analysis active: integrating hemodynamic trends, pharmacokinetic modeling, and population outcomes data for this patient profile.
ICU Bed 4  |  Post-CABG ×3  |  MR# 2026-4471
● AGENTS ACTIVE
--:--:--
MAP
72
mmHg ≥65
SBP/DBP
128/74
≤140 mmHg
Heart Rate
82
60-100 bpm
Resp Rate
14
br/min ≥10
SpO2
97
% ≥94
Temp
37.2
°C
Glucose
118
80-140 mg/dL
RASS
-1
Target -1 to 0
Vasoactive / Cardiovascular
Norepinephrine
Levophed
Vasopressor
0.08
mcg/kg/min
MAP
72 mmHg
≥65
Watching: MAP within target
Nicardipine
Cardene
Vasodilator
2.5
mg/hr
SBP
128 mmHg
≤140
Watching: SBP within target
Esmolol
Brevibloc
Rate Control
50
mcg/kg/min
Heart Rate
82 bpm
60-100
Watching: HR within target
Sedation & Analgesia
Metabolic
Propofol
Diprivan
Sedation
20
mcg/kg/min
RASS
-1
-1 to 0
Watching: RASS at target
Fentanyl
Sublimaze
Analgesia
25
mcg/hr
Resp Rate
14 br/min
≥10
Watching: RR safe
Insulin Infusion
Regular Insulin
Glycemic
2.0
units/hr
Glucose
118 mg/dL
80-140
Watching: glucose in range
🔗 AI Connectivity Engine
IHE PCD-01
HL7 v2.6 pump telemetry
HL7 v2.6PCD TF-2
ICD-10 / SNOMED
Diagnosis context binding
Z95.1CABG
FHIR R4
MedAdmin & Observation
R4 RESTSMART
Deep Learning API
Cloud outcomes model
CLOUDASYNC
ai_connectivity_engine.py | IHE/HL7/FHIR + Deep Learning
■ AI GENERATING
The Full Closed Loop — From Order Entry to Dispensing Cabinet
One Order. Eight Steps.
AI at Every Stage.
This is not a concept diagram. Every step shown below runs live against real AI in the cloud. A physician enters the post-CABG order set. Within seconds, four specialized agents query federal outcomes databases and return an evidence-based recommendation. The physician reviews it with one click. If accepted, the order routes to a pharmacy AI agent that checks dilution, renal adjustment, and drug interactions across all six active infusions before the pharmacist approves. The dispensing cabinet unlocks. The nurse is alerted. Every decision feeds back into the model. That complete sequence — live, unscripted, real cloud AI — is one click away.
Physician + AI
Order entered in CPOE. Four AI agents query CMS, AHRQ, NIH, and FDA in parallel. Evidence-based recommendation returned in seconds for one-click physician review.
Pharmacy + AI
A second AI agent verifies dilution, checks renal dose for CrCl 60-75, and scans all six active infusions for interactions before the pharmacist approves.
Dispense + Notify
Dispensing cabinet unlocked automatically. Nurse alerted via PCD-06. Smart pump library queued. No phone calls. No manual transcription. No delay.
Every Decision Trains the Model
Accept, override, or modify — every outcome is recorded. The system improves with each patient encounter. Lane keeping today. Full self-driving tomorrow.
Live Demo  ·  Real AI  ·  Not a Recording
Watch the Full Loop Run Live
Physician order to pharmacy verification to dispensing cabinet — eight steps, two AI agent calls, real cloud infrastructure. No script. No simulation. Run it yourself.
Launch Demo
Resources & Partnership

Review the Work. Start a Conversation.

The platform specification, SDK, and architecture are available for review. These are development-stage documents representing a concept that is ready for the right partner to take to production.

Development-Stage Documents
📄
AI MedAgent SDK Specification
Platform architecture, IHE PCD transaction reference, hybrid edge-cloud deployment model, incremental adoption framework, end-to-end sample code, and sandbox guide.
SDK v2.0 DEVELOPMENT STAGE PDF ↓
🎭
Strategic Partnership Presentation
The clinical problem, the inflection point, the CABG example, platform architecture with component diagram, where we are today, and the partnership opportunity.
8 SLIDES DEVELOPMENT STAGE PDF ↓
Development Stage Disclosure
These documents represent a concept platform in development. Architecture, specifications, and interfaces are illustrative. No production software or infrastructure currently exists. All IP, architecture, and designs are the property of Daniel C. Pettus.
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