Personalized Patient Experience with Digital Front Door

This repository contains code examples and utilities for implementing personalized experiences for healthcare members through a digital front door solution, leveraging AWS services like Amazon Bedrock and OpenSearch Serverless.

Overview

The code demonstrates two approaches to personalize the digital front door experience:

1. Leveraging a Vector Database: This approach uses OpenSearch Serverless as a vector database to store and retrieve personalized information based on a member's plan details.

2. Leveraging Knowledge Bases from Amazon Bedrock: This approach utilizes Amazon Bedrock's Knowledge Bases to store and retrieve personalized information based on metadata filters.

Prerequisites

Before running the code, ensure that you have the following prerequisites:

• AWS account
• Python 3.7 or later
• Required Python packages (e.g., boto3, pandas, etc.)

Installation

1. Clone the repository:

git clone https://github.com/aws-samples/aws-patient-digital-front-door.git

2. Install the required Python packages:

pip install -r requirements.txt

Architecture

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

• AWS Samples
• Amazon Bedrock Documentation
• OpenSearch Serverless Documentation