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Healthcare Appointment Agent

Overview

An AI agent for immunization related healthcare appointments built with Amazon Bedrock AgentCore Gateway using the Model Context Protocol (MCP) to expose the tools. This AI agent supports enquiring about current immunization status/schedule, checking appointment slots and booking appointments. It also provides personalized experience by knowing the logged in user (adult) and his/her children and uses AWS Healthlake as FHIR R4 (Fast Healthcare Interoperability Resources) database.

Uase case details

Information	Details
Use case type	Conversational
Agent type	Single Agent
Use case components	Amazon Bedrock AgentCore related components: Gateway and Identity
	Other Components: Amazon Cognito, AWS Healthlake, Amazon API Gateway and AWS Lambda
	MCP Tools: MCP tools are exposed to Bedrock AgentCore Gateway using OpenAPI specification
Use case vertical	Healthcare
Example complexity	Intermediate
SDK used	Amazon Bedrock AgentCore SDK and boto3

Use case Architecture

Use case key Features

Prerequisites

Note: These steps are designed to work in us-east-1 and us-west-2 regions.

Required IAM Policies

Please ensure the required IAM permissions. Ignore if running this sample from Admin role.

Cloudformation stack used in this sample has AWS Healthlake, Cognito, S3, IAM Roles, API Gateway, Lambda functions related sources.

As a quick start, you may use the combination of AWS managed IAM policies and an Inline policy to avoid issues in deploying and setting up this code sample. However it is recommended to follow the principle of privilege in production.

AWS managed IAM policies:

AmazonAPIGatewayAdministrator
AmazonCognitoPowerUser
AmazonHealthLakeFullAccess
AmazonS3FullAccess
AWSCloudFormationFullAccess
AWSKeyManagementServicePowerUser
AWSLakeFormationDataAdmin
AWSLambda_FullAccess
AWSResourceAccessManagerFullAccess
CloudWatchFullAccessV2
AmazonBedrockFullAccess

Inline Policy:

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"s3:GetObject",
				"s3:PutObject"
			],
			"Resource": [
				"arn:aws:s3:::amzn-s3-demo-source-bucket/*",
				"arn:aws:s3:::amzn-s3-demo-logging-bucket/*"
			]
		},
		{
			"Effect": "Allow",
			"Action": [
				"ram:GetResourceShareInvitations",
				"ram:AcceptResourceShareInvitation",
				"glue:CreateDatabase",
				"glue:DeleteDatabase"
			],
			"Resource": "*"
		},
		{
			"Effect": "Allow",
			"Action": [
				"bedrock-agentcore:*",
				"agent-credential-provider:*"
			],
			"Resource": "*"
		}
	]
}

Others

Python 3.12
GIT
AWS CLI 2.x
Claude 3.5 Sonnet model enabled on Amazon Bedrock. Please follow this guide to set up the same.

Use case Setup

Clone the GIT repository and navigate the the Healthcare-Appointment-Agent directory.

git clone <repository-url>
cd ./02-use-cases/05-Healthcare-Appointment-Agent/

Setup Infrastructure

Create an S3 bucket (ignore if you would like to use an existing bucket)

aws s3api create-bucket --bucket <globally unique bucket name here>

Push the lambda zip package to S3 bucket

aws s3 cp "./cloudformation/fhir-openapi-searchpatient.zip" s3://<bucket name here>/lambda_code/fhir-openapi-searchpatient.zip

Deploy cloudformation template by using below steps. The stack will take around 10 minutes. You can monitor the progress of stack by following this guide.

aws cloudformation create-stack \
  --stack-name healthcare-cfn-stack \
  --template-body file://cloudformation/healthcare-cfn.yaml \
  --capabilities CAPABILITY_NAMED_IAM \
  --region <us-east-1 or us-west-2> \
  --parameters ParameterKey=LambdaS3Bucket,ParameterValue="<bucket name here>" \
               ParameterKey=LambdaS3Key,ParameterValue="lambda_code/fhir-openapi-searchpatient.zip"

Install python dependencies and initialize the environment

Install UV as per this guide

Create and activate virtual environment

uv venv --python 3.12
source ./.venv/bin/activate

Install dependencies

uv pip install -r requirements.txt

Initialize the environment by running below command. This will create an .env file which would be used for environment variables. Use the same region name as what was used with Cloudformation template above. Note down APIEndpoint and APIGWCognitoLambdaName as returned in the output.

python init_env.py \
--cfn_name healthcare-cfn-stack \
--openapi_spec_file ./fhir-openapi-spec.yaml \
--region <us-east-1 or us-west-2>

if you need to use a named credential profile then same can be achieved with below.

python init_env.py \
--cfn_name healthcare-cfn-stack \
--region <us-east-1 or us-west-2> \
--openapi_spec_file ./fhir-openapi-spec.yaml \
--profile <profile-name here>>

The .env file should look like below.

Enable Cognito Auth with API Gateway

aws lambda invoke \
--function-name <input APIGWCognitoLambdaName as noted earlier> \
response.json \
--payload '{ "RequestType": "Create" }' \
--cli-binary-format raw-in-base64-out \
--region <us-east-1 or us-west-2>

Create some test data in AWS Healthlake

Run the below python program to ingest the test data as present in test_data folder. It may take around ~5 minutes to complete.

python create_test_data.py

Execution Instructions

Create Bedrock AgentCore Gateway and Gateway Target

Open the OpenAPI spec file fhir-openapi-spec.yaml and replace with APIEndpoint as noted down earlier.

Set up Bedrock AgentCore Gateway and Gateway Target based on OpenAPI specification in fhir-openapi-spec.yaml file. Note down the Gaeway Id from the output as it would be needed in later steps.

python setup_fhir_mcp.py --op_type Create --gateway_name <gateway_name_here>

Run Strands Agent

Run Strands Agent by using below steps.

python strands_agent.py --gateway_id <gateway_id_here>

Run Langgraph Agent