AI医疗系统

📋 Prerequisites & System Requirements

Before running the application, ensure your environment meets the following specifications:

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Set up python dependencies

python -m pip install -r requirements-full.txt

#### 2️⃣ Configure Environment Variables via Doppler
Instead of manually managing `.env` files, we use **Doppler** as our centralized secret manager.

First, install the [Doppler CLI](https://docs.doppler.com/docs/install-cli). Then, authenticate to your workspace:

*(Once logged in, the application will automatically fetch all environment variables, including `DATABRICKS_TOKEN` and database URIs, directly from Doppler)*.

#### 3️⃣ Bootstrap Database & Seed Mock Data (Instant Activation)
Run the clinical integration runner script to automatically create your database schema and seed mock clinicians, appointments, diagnostic logs, and patient records:

*(This creates `healthcare.db` and populates active patient profiles so your dashboard isn't empty on first login!)*.

#### 4️⃣ Launch Local Private LLM (Ollama Setup)
To run fully offline clinical RAG and chatbot queries:
1. Download and install [Ollama](https://ollama.com/).
2. Start the Ollama background service and download the default models in your terminal:

#### 5️⃣ Start Dev Servers (Backend & Frontend)
Launch the FastAPI server and the React dev compiler:

* **Backend API (Terminal 1)**:
  

* **React Web Portal (Terminal 2)**:
  

☁ 5 Deployment Options

Docker Compose, Enterprise Stack (7 services), Render PaaS, Kubernetes (3-replica HA), Terraform AWS (VPC + EKS + RDS + ElastiCache).

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5. Deployment, Operations & Security

• Performance Benchmarks &mdash; Operational SLAs, response latencies, and high-throughput EKS scaling metrics.
• Real-Time Monitoring Patterns &mdash; Prometheus metrics exporters, Grafana dashboard alerts, and WS broadcast architectures.
• Backup and Restore Readiness &mdash; Disastrous recovery runbooks, Point-in-Time Recovery (PITR) configurations, and replication sets.
• Incident Response Readiness &mdash; Security breach playbooks, reporting procedures, and recovery SLA limits.
• Security Assurance Readiness &mdash; OS hardening playbooks, network firewalls, and credential rotation calendars.
• Security Questionnaire &mdash; Comprehensive institutional questionnaire (HECVAT/HIPAA alignment) for hospital IT boards.

2. Launch with Docker Compose

Launches the complete service container stack (FastAPI backend + React frontend + PostgreSQL + Redis) in a single command:

git clone https://github.com/pavanbadempet/AI-Healthcare-System.git
cd AI-Healthcare-System
doppler login                 # Authenticate with Doppler
doppler run -- docker compose up --build

3. Local Developer Sandbox Setup (5-Step Walkthrough)

To get your workspace running locally with mock user and patient records, follow these five steps:

#### 1️⃣ Clone and Install Python Dependencies ```bash

⚡ 30-Second Quickstart (1-Click Interactive Demo)

Experience the entire clinical intelligence stack locally with a single command (auto-provisions mock EHR database, pre-loads TabICLv2 & Quad-Ensemble weights, and opens the React 19 workstation):

```bash

⚡ Quick Start

Run with Bun (Instant 1-Click Interactive Demo)

bun run demo

⚙ Environment Configuration Reference

Create a .env file in the project root based on the table below:

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🔄 Zero-Configuration Developer Fallback Modes

To allow the platform to run seamlessly on developer machines and thin CI/CD environments (such as GitHub Actions) without requiring the installation of proprietary/external enterprise dependencies (clinical-rag-cache and clinical-fhir-abdm), the backend features a robust local fallback system:

• Fully-Featured Local RAG & Vector Store Fallback: Automatically switches to a persistent JSON-based vector store in backend/rag.py featuring LSH (Locality Sensitive Hashing) Approximate Nearest Neighbor indexing and hybrid similarity scoring.
• Persistent Semantic Cache Fallback: Implements a local file-based SemanticCache using cosine similarity matching over embeddings to optimize and cache LLM responses locally.
• Fully-Schema-Compliant FHIR/ABDM fallbacks: Generates fully validated FHIR R4 Resources (Patient, Observation, MedicationRequest, Invoice, CareEvent, etc.) with local reference resolution and bundle validation, satisfying rigorous automated verification tests out-of-the-box.
• Auto-Initializing Database Schema: Automatically creates and initializes the SQLite schema (Base.metadata.create_all) for all standalone audit runners and tests if a local fallback database is used, guaranteeing that clinical agents can run instantly without manual migrations.

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📡 Complete REST API Contract

The FastAPI backend exposes the following REST endpoints:

⚙ 8 CI/CD Pipelines

Pytest + coverage, CodeQL SAST, Docker GHCR builds, HuggingFace sync, Dependabot, release drafter, stale bot, and Render keep-alive.

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Built for enterprise, built for production. This is a production-grade clinical intelligence platform demonstrating advanced ML engineering, LLM orchestration, RAG architecture, and DevOps maturity in a single cohesive codebase.

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🤖 Clinical AI Workflow Automation & System Telemetry

AI Healthcare System features domain assistant pipelines and telemetry layers for clinical precision and operational governance.

2. Clinical & Medical Workflows

• Diagnostics Workflow &mdash; Clinical lab metric intake, inference pipeline, SHAP interpretability plot rendering, and physician validation.
• Nursing Workflow &mdash; Nurse worklist coordinators, vital sign logs, and shift handover data packets.
• Pharmacy Workflow &mdash; E-prescription fulfillment, drug-drug interaction validation checks, and automatic reorder thresholds.
• Billing Workflow &mdash; Insurance claims verification, double-entry transactional ledgers, and payment processing loops.
• Discharge Workflow &mdash; Inpatient release checklists, outpatient referral summaries, and automatic FHIR serialization.
• Hospital Operations Core &mdash; Inpatient/Outpatient (IPD/OPD) coordinate systems, bed allocation logic, and clinic occupancy algorithms.

Interoperability & Integration Standards

• GET /v1/interop/patient/fhir-bundle: Export patient record as FHIR R4 JSON bundle.
• POST /v1/interop/patient/consents: Grant interoperability access consent.
• POST /v1/interop/patient/consents/{consent_id}/revoke: Revoke granted data consent.
• GET /v1/interop/abdm/readiness: Check India ABDM integration sandbox readiness.
• POST /v1/interop/abdm/consent-callbacks: Ingest ABDM consent lifecycle event.
• GET /v1/interop/dicomweb/readiness: Verify DICOM PACS connection status.
• GET /v1/interop/smart/readiness: Verify SMART on FHIR authorization client status.

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🚀 CI/CD Pipelines Registry

We run 8 structured GitHub Actions workflows for continuous integration and compliance:

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🆚 Competitive Comparison: Why AI Healthcare System?

| ABDM Digital Health Stack | ✅ Active Consent Lifecycle & Sandboxing | ❌ Third-party plugins | ❌ None | ❌ Enterprise integration required | | Modern Telemetry Broadcasting| ✅ Live WebSockets Broadcasts | ❌ None | ❌ None | ❌ Batch reporting only |

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