FL Integrated Autonomous Agentic AI

[refai06]

Concept
High-Level Idea:
Agentic AI Personal Health Coach (Post-Discharge Companion):
A digital health agent, powered by an LLM and deployed within each hospital or clinic, provides personalized recovery support to discharged patients.
These agents are privacy-preserving, autonomous, and continuously learning across institutions using Federated Learning.

Business Problem Being Solved

• Current Challenges:
  • High readmission rates (especially for chronic conditions like heart disease, diabetes).
  • Lack of personalized care after discharge, leading to complications.
  • Patient non-adherence to medication or post-op care.
  • Data silos and privacy regulations prevent hospitals from sharing patient records.

Value Delivered

• Personalized, continuous care improves outcomes and patient satisfaction.
• FL ensures collaborative learning without violating privacy (no raw data sharing).
• AI agents automate repetitive tasks (follow-ups, reminders), saving nurse/doctor time.
• Hospitals reduce readmission penalties, and clinics can offer premium digital services.

Technical Architecture

• Core Components:
  Component Description
  Local Agentic AI An LLM-powered agent deployed in each hospital, acting as the digital health companion.
  LLMs Fine-tuned models like OpenChat, LLaMA, or GPT variants (on private infrastructure).
  Prompt Engineering Used to scaffold reasoning paths, guide conversation tone, and ensure medical safety.
  Federated Learning Model updates (gradients, not patient data) shared across institutions for collective intelligence.
  Electronic Health Record (EHR) Integration Pulls in clinical data with patient consent to contextualize recommendations.
  Mobile/Web App Patient interface for interaction, alerts, vitals input, reminders.

How It Works — Step-by-Step

1. Deployment
   Each hospital/clinic runs a local AI agent on-prem or in a secure edge/cloud environment.
   The agent is connected to the hospital’s EHR system (Electronic Health Record).

2. LLMs + Prompt Engineering
   Prompts are crafted based on:

   • Medical condition (e.g., “You’re recovering from cardiac surgery.”)
   • Personality tuning (friendly, non-alarming, culturally sensitive).
   • Actionable goals (e.g., "Help patient manage blood pressure via daily check-ins.")

3. Multi-Agent Planning
   The AI isn't just chat-based. It acts autonomously:

   • Schedules telehealth if symptoms worsen.
   • Sends reminders for follow-ups and tests.
   • Queries internal hospital systems for lab reports.
   • Flags anomalies (e.g., sudden weight gain) and alerts the care team.

4. Federated Learning Integration

• Each hospital trains its own model on local data (e.g., patient recovery outcomes).
• Periodically, encrypted model updates are shared to a central FL coordinator.
• A global model is formed and redistributed — improving prediction of:
  • Medication adherence risk.
  • Early signs of infection or readmission.
  • Optimal recovery timelines.
  • Differential privacy and secure aggregation prevent reverse-engineering patient data.

Business Benefits (Quantified)
Benefit Metric
Lower readmissions 15–25% reduction possible (esp. chronic care)
Reduced care team load Automates 30–50% of follow-up conversations
Premium service Hospitals can charge for AI-assisted recovery plans
Regulatory safety Privacy by design; avoids liability from data leakage

Differentiators vs. Traditional Solutions
Traditional Follow-Up Agentic AI Coach
Manual phone calls 24/7 intelligent assistant
One-size-fits-all leaflets Personalized advice
Risk of data breach if shared Federated & private
Reactive (after problems) Proactive + predictive

Expansion Potential
- Chronic Disease Programs (e.g., diabetes, COPD).
- Elderly Care Coordination.
- Mental Health Coaching.
- Cross-border clinical trials using FL-enabled learning from different geographies.

Patient Interaction Example

• EXAMPLE 1: Post-Cardiac Surgery Patient (Heart Bypass)

  • Goal:
    Avoid readmission due to post-surgery complications and ensure medication/diet adherence.

  • Interaction Flow:
    Daily Check-In (AI Agent on Mobile App):
    Agent: "Good morning, Mr. Sharma. How did you sleep last night? Any discomfort or chest pain today?"
    Patient: "I slept okay, but I felt a little breathless climbing stairs."
    Agent:
    "Thank you for sharing. That could be normal in early recovery, but I’d like to alert your care team just to be sure. Meanwhile, please avoid stairs today.
    Shall I book a teleconsultation for tomorrow morning?"

  • Behind the scenes:
    Agent flags data in EHR.
    Schedules doctor call.
    Logs symptom for FL model refinement (without sharing raw data).

• EXAMPLE 2: Type 2 Diabetic Patient Discharged After Blood Sugar Spike

  • Goal:
    Avoid another ER visit; maintain sugar levels via diet and medicine tracking.

  • Interaction Flow:
    Post-lunch Reminder:
    Agent: "Hi Asha, did you take your metformin 500mg after lunch?"
    Patient: "No, I forgot."
    Agent:
    "Thanks for letting me know. I’ve set up a daily reminder post-lunch. Also, remember to drink plenty of water today.
    Do you want to log your glucose reading now?"
    Diet Alert:
    Patient logs: High-carb meal.
    Agent:
    "That’s a little high in carbs. Based on your recovery plan, let’s aim for more vegetables and proteins.
    Shall I suggest some recipes from your dietician's list?"

  • Behind the scenes:
    Medication compliance model flags high risk → contributes to FL update.
    Agent creates positive reinforcement loop (coach-like tone).

• EXAMPLE 3: Elderly Hip Replacement Recovery at Home

  • Goal:
    Prevent falls, assist with exercises, monitor pain levels.

  • Interaction Flow:
    Daily Physical Therapy Video Prompt:
    Agent: "Hi Mr. Kulkarni, it's time for your hip-strengthening exercises. Would you like me to walk you through them with today’s 4-minute video?"
    Fall Risk Alert:
    Patient types: "Felt dizzy after standing up."
    Agent:
    "I’ve logged this as a postural drop. Please sit for a few minutes and avoid walking unaided. I’m notifying your physiotherapist."

  • Behind the scenes:
    FL improves fall risk detection across elderly users.
    Agent adjusts recovery timeline suggestions based on progress.

• EXAMPLE 4: Young Woman Recovering from COVID & Anxiety

  • Goal:
    Support both physical and mental recovery.

  • Interaction Flow:

  • Nightly Emotional Check-In:
    Agent: "You mentioned feeling anxious before bedtime last week. Would you like to do a 3-minute guided breathing session tonight?"
    Encouragement Loop:
    Patient: "Yes please. Also, I feel a bit overwhelmed today."
    Agent:
    "That’s okay — recovery takes time. You’ve come a long way already. I’ll also notify your therapist for a possible check-in tomorrow."

  • Behind the scenes:
    Local models detect mood drift from conversation pattern.
    FL contributes to emotional wellness trend tracking across regions.