A. Problem Statement: Missed post-discharge follow-ups and lack of effective at-home recovery monitoring are primary drivers of preventable hospital readmissions. This results in: • High Costs: Billions in avoidable healthcare spending. • Poor Outcomes: Increased patient morbidity and mortality. • Inefficient Use of Resources: Overburdened hospital staff managing administrative follow-ups. B. Solution: An Autonomous, Multi-Agent AI System CareCompanion is an intelligent, autonomous AI agent designed to bridge the gap between hospital discharge and full patient recovery. It acts as a continuous, personalized health coach and early warning system. Feature Core Functionality Impact Continuous Vitals Monitoring Tracks physiological data (HR, sleep, activity) via IoT/Wearable APIs (e.g., Apple HealthKit, Garmin). Provides objective, real-time recovery data. Anomaly Detection & Risk Scoring Gen AI/LLM analyses unstructured patient conversation data, while ML models analyse vital trends. Detects subtle signs of decline (e.g., increased fatigue, persistent fever) and assigns a dynamic risk score. Proactive Intervention & Scheduling Uses Reinforcement Learning (RL) to determine the optimal next step (e.g., a reassuring text, a medication reminder, or scheduling an urgent tele consult). Prevents a minor issue from becoming a crisis, optimizing clinician time. Conversational Support LLM-powered Chatbot provides empathetic, on-demand answers to common post-discharge questions (diet, wound care, expected symptoms). Reduces calls to hospital staff and improves patient education and adherence. C. Tech Stack Overview: Data & Device Integration: • APIs: Apple HealthKit, Google Fit, Garmin SDK • Standards: HL7 FHIR for secure EHR interoperability Backend & Infrastructure: • Frameworks: FastAPI (Python), Node.js • Databases: PostgreSQL, TimescaleDB (For time series data like vitals), Vector DB (Pinecone/FAISS) • Cloud: Google Cloud / AWS (GKE / Cloud Run) AI & Analytics Layer: • LLMs: OpenAI / Llama 3 for conversational intelligence • Orchestration: LangChain (or LangGraph / LangSmith) for building agent workflows, memory, tools, and connecting LLMs to external data/APIs • ML Frameworks: PyTorch, TensorFlow, scikit-learn, Time-Series libraries, Anomaly detection (Isolation Forest, LSTM-autoencoders, Prophet or ARIMA) • Reinforcement Learning: Stable-Baselines3 / RLlib for proactive intervention policy Frontend Applications: • Patient App: React Native / Flutter • Provider Dashboard: React.js / Next.js Security & Compliance: • End-to-end encryption (AES-256, TLS 1.2+) • HIPAA / GDPR-ready infrastructure with OAuth2 / Smart on FHIR authentication D. Pain Points Addressed: • Missed post-discharge follow-ups • Late detection of complications • High burden on hospital staff • Low patient engagement and adherence Value Proposition: Metric Current Reality CareCompanion Impact Readmission Rate Average ≈15% to 20% within 30 days. Target Reduction: 50% to 70% of preventable readmissions. Cost of Readmission Average ≈$15,000 per readmission. Savings: Hundreds of thousands for a mid-sized hospital annually. Patient Adherence Often <50% for complex medication regimens. Target Adherence: >85% via personalized nudges. E. Our Target Audience: • Primary Audience: o Hospitals and healthcare systems o Home healthcare providers, Nutritionists o Patients with chronic or post-surgical recovery needs • Secondary Audience: o Health insurers and TPAs (to reduce claim costs) o Wearable tracking device manufacturers / IoT device companies F. Revenue & GTM: • Primary Revenue (B2B SaaS): o Tiered Subscription: A fixed monthly fee per enrolled patient ($X per patient or per bed per month). Tiers based on feature set (Standard vs. Premium, which includes RL personalization). o Value-Based Pricing: Potential for a contract that offers a $XX rebate/discount to the provider if the readmission rate for CareCompanion-managed patients exceeds a certain threshold. • B2C Add-on: o More premium features for individual patients. • Secondary Revenue: o Device Integration Licensing: Charging wearable manufacturers, a fee to be officially “CareCompanion Certified", guaranteeing smooth data flow. o Partnerships with health insurers o Data Aggregation (Anonymized): Licensing anonymized, aggregated population health data to researchers or pharmaceutical companies.