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‎clinical-assistant/.env.example‎

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# Langfuse Configuration for Clinical Assistant Tracing
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# Get these keys from your Langfuse project settings: https://cloud.langfuse.com
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# Required for Langfuse tracing
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LANGFUSE_PUBLIC_KEY=pk-lf-your-public-key-here
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LANGFUSE_SECRET_KEY=sk-lf-your-secret-key-here
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# Langfuse host (choose your region)
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LANGFUSE_HOST=https://cloud.langfuse.com # EU region (default)
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# LANGFUSE_HOST=https://us.cloud.langfuse.com # US region
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# Optional: Clinical session and provider identification for better tracing
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CLINICAL_SESSION_ID=clinical-session-001
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HEALTHCARE_PROVIDER_ID=[email protected]
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# Note: Copy this file to .env and fill in your actual values
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# The application will work without these variables but tracing will be disabled

‎clinical-assistant/README.md‎

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# clinical-assistant
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Healthcare professionals face increasing challenges in managing complex patient data and making timely, accurate decisions. This lab demonstrates how to build a clinical assistant using AWS technologies that can assist providers by integrating multiple data sources and AI capabilities.
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As a healthcare provider, we want an AI agent that analyzes a patient’s current symptoms, medical history, and existing documentation to recommend an accurate and personalized course of action. The agent shall help with the following:
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- Ingest and understand the patient’s current symptoms and complaints.
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- Retrieve relevant historical medical records and documentation through MCP servers.
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- Utilize AWS Strands SDK to orchestrate the AI agent’s reasoning, planning, and tool invocation, enabling dynamic and flexible decision-making workflows.
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- Run Agents, LiteLLM and MCP servers on EC2 instances powered by Amazon EKS, optimizing cost and performance.

‎clinical-assistant/app.py‎

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: MIT-0
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# Adapted from https://github.com/kyopark2014/strands-agent
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# SPDX-License-Identifier: MIT
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import logging
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import sys
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import streamlit as st
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import health_agent_async
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logging.basicConfig(
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level=logging.INFO, # Default to INFO level
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format="%(filename)s:%(lineno)d | %(message)s",
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handlers=[logging.StreamHandler(sys.stderr)],
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)
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logger = logging.getLogger("streamlit")
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# title
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st.set_page_config(
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page_title="Clinical Decision Support AI Assistant",
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page_icon="🏥",
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layout="centered",
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initial_sidebar_state="auto",
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menu_items=None,
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)
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with st.sidebar:
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st.title("Clinical Assistant Menu")
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st.markdown(
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"**Clinical Decision Support AI Assistant** with access to patient data through MCP healthcare server. "
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"This assistant helps healthcare professionals with diagnostic reasoning and clinical decision-making. "
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)
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# Healthcare-specific information
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st.markdown("### Available Patient Data")
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st.markdown("""
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**Sample Patient IDs:**
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- PAT001, PAT002, PAT003
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**Available Tools:**
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- Patient Demographics
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- Medical History
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- Lab Results
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- Patient Search
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- Risk Assessment
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""")
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st.markdown("---")
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st.markdown("### ⚠️ Important Notice")
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st.markdown("""
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This is a **clinical decision support tool** for healthcare professionals only.
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- Not a replacement for clinical judgment
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- Final decisions must be made by qualified healthcare professionals
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- Maintains HIPAA compliance principles
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""")
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clear_button = st.button("Reset Conversation", key="clear")
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st.title("🏥 Clinical Decision Support AI Assistant")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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st.session_state.greetings = False
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# Display chat messages from history on app rerun
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def display_chat_messages():
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"""Print message history
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@returns None
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"""
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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if "images" in message:
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for url in message["images"]:
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logger.info(f"url: {url}")
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file_name = url[url.rfind("/") + 1 :]
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st.image(url, caption=file_name, use_container_width=True)
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st.markdown(message["content"])
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display_chat_messages()
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# Greet user
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if not st.session_state.greetings:
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with st.chat_message("assistant"):
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intro = """👋 Welcome to the Clinical Decision Support AI Assistant!
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I'm here to help healthcare professionals with:
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- **Diagnostic reasoning** and differential diagnoses
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- **Patient data analysis** using our MCP healthcare server
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- **Clinical insights** based on medical guidelines
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- **Treatment recommendations** considering patient history
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- **Lab result interpretation** and clinical findings
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I have access to patient data for sample patients (PAT001, PAT002, PAT003) and can help with clinical decision-making.
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**Important:** I'm a support tool for healthcare professionals, not a replacement for clinical judgment. All recommendations are for clinical decision support only.
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How can I assist you with your clinical analysis today?"""
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st.markdown(intro)
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": intro})
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st.session_state.greetings = True
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if clear_button or "messages" not in st.session_state:
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st.session_state.messages = []
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st.session_state.greetings = False
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st.rerun()
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# Always show the chat input
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if prompt := st.chat_input("Enter your clinical question or patient case..."):
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with st.chat_message("user"): # display user message in chat message container
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st.markdown(prompt)
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st.session_state.messages.append(
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{"role": "user", "content": prompt}
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) # add user message to chat history
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prompt = prompt.replace('"', "").replace("'", "")
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logger.info(f"Clinical prompt: {prompt}")
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with st.chat_message("assistant"):
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response = health_agent_async.run_health_agent(prompt, st)
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st.session_state.messages.append({"role": "assistant", "content": response})

‎clinical-assistant/cleanup-langfuse.sh‎

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#!/bin/bash
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# Script to clean up Langfuse deployment
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set -e
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# Color codes for better readability
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GREEN='\033[0;32m'
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BLUE='\033[0;34m'
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RED='\033[0;31m'
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YELLOW='\033[0;33m'
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NC='\033[0m' # No Color
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# Function to display messages with timestamp
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log() {
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echo -e "${BLUE}[$(date '+%Y-%m-%d %H:%M:%S')]${NC} $1"
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}
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warn() {
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echo -e "${YELLOW}[$(date '+%Y-%m-%d %H:%M:%S')] WARNING:${NC} $1"
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}
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success() {
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echo -e "${GREEN}[$(date '+%Y-%m-%d %H:%M:%S')] SUCCESS:${NC} $1"
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}
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log "Uninstalling Langfuse Helm release..."
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helm uninstall langfuse || warn "Failed to uninstall Langfuse Helm release, it may not exist"
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log "Deleting Langfuse PVCs..."
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kubectl delete pvc data-langfuse-clickhouse-shard0-0 data-langfuse-clickhouse-shard0-1 data-langfuse-clickhouse-shard0-2 data-langfuse-postgresql-0 data-langfuse-zookeeper-0 data-langfuse-zookeeper-1 data-langfuse-zookeeper-2 langfuse-s3 valkey-data-langfuse-redis-primary-0 || warn "Failed to delete some PVCs, they may not exist"
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log "Waiting for resources to be cleaned up (30 seconds)..."
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sleep 30
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success "Langfuse cleanup completed!"
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log "You can now run setup.sh to reinstall Langfuse with the auto-ebs-sc storage class"

‎clinical-assistant/custom-nodepool.yaml‎

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apiVersion: karpenter.sh/v1
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kind: NodePool
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metadata:
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name: custom
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spec:
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template:
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spec:
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expireAfter: 168h
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nodeClassRef:
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group: eks.amazonaws.com
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kind: NodeClass
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name: default
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requirements:
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- key: karpenter.sh/capacity-type
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operator: In
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values: ["spot", "on-demand"]
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- key: "eks.amazonaws.com/instance-category"
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operator: In
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values: ["c", "m", "r"]
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- key: "eks.amazonaws.com/instance-generation"
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operator: Gt
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values: ["5"]
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- key: kubernetes.io/arch
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operator: In
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values: ["amd64", "arm64"]
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limits:
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cpu: 1000
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memory: 1000Gi
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disruption:
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consolidationPolicy: WhenEmptyOrUnderutilized
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consolidateAfter: 1m
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