Personalized patient experience with digital front door

In today’s data-driven world, healthcare payors have a unique opportunity to leverage the power of personalization to drive better health outcomes and member satisfaction. In our previous Digital Front Door blog for Healthcare Payors, we explored how AWS services can enable a comprehensive digital front door experience for members. As Payors begin prototyping these solutions, a common challenge arises: how to personalize the conversational AI experience to each member’s specific health plan. Healthcare Payors typically offer multiple insurance products, each with varying deductibles, copays, and coverage details. When a member inquires about plan-specific information through the chatbot, it’s crucial to accurately reference their enrolled product and provide the corresponding details. Inaccurate or inconsistent responses could lead to member dissatisfaction, compliance issues, and potential financial implications.

In this blog, we will demonstrate how to personalize the digital front door experience for healthcare members. By leveraging Amazon OpenSearch Serverless, either directly or through the Knowledge Bases for Amazon Bedrock service, we can efficiently store and retrieve patient-specific plan information. This enables us to deliver accurate and tailored content, ensuring a seamless and compliant digital experience for our members.

Enabling personalized healthcare experiences with AWS

To deliver a truly personalized experience to members, we need to filter and present relevant information based on their specific health plan. The following reference architecture leverages OpenSearch Serverless via Knowledge Bases for Amazon Bedrock as a vector database and demonstrates the workflow for achieving this level of personalization:

1. A member initiates a request for plan-specific information, such as deductible, copay, or coverage details, through the digital front door interface.

2. Amazon API Gateway, acting as the entry point, forwards the request along with the member’s identification to an AWS Lambda function dedicated to user profile management.

3. The Profile Lambda function queries the User Profile database, an Amazon DynamoDB table, to retrieve the mapping between the member’s ID and their enrolled health plan.

4. The Profile Lambda updates the metadata filter for the Amazon OpenSearch Serverless vector database with the member’s plan information. This filter enables precise content filtering based on the member’s plan details.

5. Amazon OpenSearch Serverless through Knowledge Bases for Amazon Bedrock, AWS’s service for building enterprise-scale knowledge repositories, integrates the metadata filter at the retrieval level when querying the vector database. This ensures that the lookup operation retrieves accurate and personalized results specific to the member’s enrolled health plan, rather than returning generic information across all plans.

By leveraging AWS services such as Amazon API Gateway, AWS Lambda, Amazon OpenSearch Serverless, and Amazon Bedrock, healthcare payors can implement a robust and scalable solution for delivering personalized experiences through their digital front door. This approach not only enhances member satisfaction by providing tailored information, but also mitigates potential compliance risks and financial implications associated with inaccurate or inconsistent responses.

Demystifying metadata filtering: a practical example

Let’s consider a scenario where a healthcare payor offers three distinct health plans: a Preferred Provider Organization (PPO) Plan, a Health Maintenance Organization (HMO) Plan, and a High Deductible Health Plan (HDHP). While payors typically offer a extensive range of plans, this simplified example will help illustrate the concept of metadata filtering and its importance in delivering personalized experiences.

The table below summarizes the key benefits associated with each of the three plans:

As you can observe, each plan has varying deductible amounts, copays, and coinsurance rates. While the language used to describe these benefits may be similar across plans, the numeric values vary widely, often differing by hundreds or even thousands. It is crucial to accurately identify the member’s enrolled plan and provide the corresponding details to ensure a personalized and compliant experience.

When ingesting plan-specific information into the Amazon OpenSearch Serverless vector database, we associate each data chunk with relevant metadata, including the plan identifier. During the retrieval process, Knowledge Bases for Amazon Bedrock integrates the member’s plan metadata as a filter, ensuring that the returned results are tailored to their specific plan.

For example, if a member enrolled in the PPO Plan inquires about their deductible, the metadata filter will restrict the search to only those data chunks associated with the PPO Plan metadata. This precise filtering mechanism guarantees that the member receives the accurate deductible amount of $1,000, rather than the deductible values corresponding to the HMO Plan or HDHP.

By leveraging metadata filtering capabilities within AWS services like Amazon OpenSearch Serverless and Knowledge Bases for Amazon Bedrock, healthcare payors can effectively manage and serve personalized content, enhancing member satisfaction and mitigating potential compliance risks arising from inaccurate or inconsistent information.

Unlocking personalized Knowledge Bases with AWS

In the previous section, we explored the concept of metadata filtering and how it enables personalized content delivery. Now, let’s dive into the technical implementation of this approach using Amazon Bedrock’s Knowledge Bases.

Amazon Bedrock’s Knowledge Bases provide a powerful solution for organizing and serving enterprise-wide knowledge repositories. By leveraging metadata filtering capabilities, we can tailor the search results to match each member’s specific health plan, ensuring a truly personalized experience.

To enable metadata filtering within Amazon Bedrock’s Knowledge Bases, we need to associate each document with relevant metadata attributes during the ingestion process. This metadata will include identifiers for the specific health plan, allowing us to filter the search results based on the member’s enrolled plan.

The metadata for each document should be provided in a separate JSON file with a specific naming convention: <filename>.metadata.json. For example, if the document file is named HMO_Plan.pdf, the corresponding metadata file will be named HMO_Plan.pdf.metadata.json.

The metadata JSON file should follow a predefined structure, with a metadataAttributes object containing key-value pairs for the relevant metadata fields. Here’s an example of how the metadata files could be structured for three different health plans:

In this example, each metadata file includes attributes such as the plan identifier (id), file name, document type (type), and plan year. These attributes can be customized based on your specific requirements and the information you need to associate with each plan document.

During the ingestion process, Amazon Bedrock will automatically associate the metadata from the JSON files with the corresponding documents, enabling efficient filtering and retrieval of plan-specific information.

The following steps outline configuring and applying filters to Knowledge Base data, allowing for plan-specific responses:

1. Retrieve User Profile: When a member initiates a query, retrieve their user profile from the User Profile database (e.g., an Amazon DynamoDB table) to determine their enrolled health plan.

2. Set Filter Parameters: Based on the user’s enrolled plan, set the appropriate filter parameters for the Knowledge Base. These filters will typically be based on the filenames or metadata associated with each plan’s documentation.

3. Apply Retrieval Configuration: Configure the Knowledge Base retrieval process to apply the designated filters. This step ensures that the search operation only considers relevant data chunks associated with the member’s specific plan.

4. Invoke Retrieval and Generation: Invoke the ‘retrieve_and_generate’ operation on the Knowledge Base, which performs the following functions:

a. Apply the specified filter(s) to the search query.
b. Query the Knowledge Base with the applied filters, retrieving only the relevant plan-specific information.
c. Send the filtered results to the designated Large Language Model (LLM) for summarization and generation of a personalized response.

To illustrate the personalization capabilities, let’s consider a scenario where members from different health plans inquire about their family deductible:

By leveraging the metadata filtering capabilities of Amazon Bedrock’s Knowledge Bases, each member receives a personalized response tailored to their specific health plan, ensuring accurate and compliant information delivery.

To explore a sample implementation of this solution, including code snippets and detailed instructions, please refer to our GitHub repository.

Enabling personalized search with Amazon OpenSearch Serverless

Another approach to enabling personalization involves leveraging an Amazon OpenSearch Serverless vector database directly for efficient storage and retrieval of plan-specific information. The personalization process when using Amazon OpenSearch Serverless directly is similar to accessing it through Knowledge Bases, but with a few key differences.

Instead of creating separate metadata files, the metadata is ingested directly through APIs along with the actual document content. Here is a high-level overview of how to apply personalization filters to Amazon OpenSearch Serverless directly:

1. Retrieve User Profile: When a member initiates a query, retrieve their user profile from the User Profile database (e.g., an Amazon DynamoDB table) to determine their enrolled health plan.

2. Apply OpenSearch KNN Filters: Based on the user’s enrolled plan, apply the appropriate K-Nearest Neighbor (KNN) filters in Amazon OpenSearch Serverless. These filters will be based on the plan name or other relevant metadata attributes associated with each document.

3. Integrate Filters in LangChain: Within the LangChain library, integrate the filter obtained in the previous step to ensure that the similarity search operation within Amazon OpenSearch Serverless only retrieves documents associated with the member’s specific plan.

By leveraging the filtering capabilities of Amazon OpenSearch Serverless and integrating them with the LangChain library, healthcare payors can create a seamless and personalized experience for their members. This approach empowers members to clarify complex policy details efficiently, improving overall satisfaction and reducing friction within the healthcare system.

For a detailed implementation guide, including code samples and step-by-step instructions, please refer to our GitHub repository.

Scaling personalization with Serverless Architectures

As healthcare payors embrace digital transformation and strive to deliver personalized experiences at scale, the ability to handle fluctuating workloads and seamlessly scale becomes paramount. This is where the power of serverless architectures, combined with AWS services, can be leveraged to ensure a highly available, cost-effective, and scalable personalization solution.

By leveraging serverless services like AWS Lambda, Amazon API Gateway, and Amazon DynamoDB, payors can build a robust and event-driven architecture that automatically scales to meet demand. This approach eliminates the need for provisioning and managing servers, allowing you to focus on delivering a seamless member experience while benefiting from the inherent scalability and cost optimization of serverless computing.

For instance, when a member initiates a request through the digital front door, Amazon API Gateway can act as the entry point, invoking an AWS Lambda function responsible for retrieving the user’s profile and enrolled plan. This Lambda function can then interact with Amazon DynamoDB, a fully managed NoSQL database service, to fetch the necessary plan information.

Once the plan details are obtained, the Lambda function can invoke another serverless component, such as Amazon OpenSearch Serverless or Amazon Bedrock’s Knowledge Bases, to perform the personalized search or retrieval operation. These services can seamlessly scale to handle bursts of traffic, ensuring that members receive prompt and accurate responses, regardless of the concurrent request volume.

By embracing serverless architectures, healthcare payors can achieve the following benefits:

1. Automatic Scaling: Serverless services automatically scale up or down based on incoming traffic, ensuring optimal performance and resource utilization.

2. Cost Optimization: With serverless computing, you only pay for the resources consumed during execution, eliminating the need for idle infrastructure.

3. High Availability: AWS services are designed for high availability, ensuring that your personalization solution remains accessible and resilient.

4. Rapid Iteration: Serverless architectures enable faster development cycles, allowing you to quickly iterate and enhance your personalization capabilities.

By leveraging AWS services like AWS Lambda Layers and AWS CodeDeploy, you can streamline the deployment and management of your personalization solution, ensuring seamless updates and minimizing downtime.

By embracing serverless architectures and leveraging the power of AWS services, healthcare payors can deliver personalized experiences at scale, while benefiting from cost optimization, high availability, and rapid innovation cycles.

Next steps: elevating personalized healthcare experiences

The approach outlined in this blog demonstrates how healthcare payors can leverage Amazon OpenSearch Serverless, both directly and through Knowledge Bases for Amazon Bedrock, to deliver personalized information to members through their digital front door solutions. While this is a significant step forward, there are several potential next steps to further enhance the member experience and drive continuous improvement:

1. Omnichannel Integration: Integrate the personalized information retrieval API with call center systems, mobile applications, and other touchpoints. This omnichannel integration ensures that members receive consistent, accurate, and personalized information across various channels, providing a seamless and cohesive experience.

2. Expanded Personalization Factors: While the current implementation focuses on personalizing information based on the member’s plan, payors could explore incorporating additional personalization factors, such as demographics, health conditions, preferences, or behavioral data. By leveraging these additional data points, the system can provide even more tailored and relevant information, enhancing the overall member experience.

3. Continuous Learning and Improvement: Implement mechanisms for continuous learning and improvement, such as active learning or reinforcement learning techniques. Active learning identifies areas of uncertainty and prompts human experts for feedback, while reinforcement learning enables the system to learn from the outcomes of its responses and refine its performance over time.

4. Multimodal Interactions: Explore the integration of multimodal interactions, such as voice or visual interfaces, in addition to text-based interactions. This caters to diverse preferences and accessibility needs, further enhancing the overall user experience and ensuring that no member is left behind.

5. Personalized Recommendations: Leverage the personalized member data and insights to provide tailored recommendations for wellness programs, preventive care, or other health-related services. This proactive approach empowers members to take control of their health and well-being while fostering a deeper relationship with the payor.

6. Privacy and Compliance: Continuously evaluate and enhance privacy and compliance measures to ensure that member data is handled securely and in accordance with relevant regulations, such as HIPAA. Implement robust access controls, encryption, and auditing mechanisms to maintain trust and transparency.

By implementing these next steps, healthcare payors can continue to improve the personalized member experience, increase operational efficiency, and contribute to better overall healthcare outcomes. Embracing a culture of continuous improvement and leveraging the latest technologies will be crucial in staying ahead of evolving member expectations and delivering exceptional healthcare experiences.

Conclusion

In today’s consumer-centric healthcare landscape, delivering personalized experiences to members is no longer a luxury but a critical requirement for healthcare payors. By providing accurate and relevant information about coverage and benefits, payors can foster trust, enhance member satisfaction, and ultimately contribute to better healthcare outcomes.

This blog demonstrated how payors can leverage the power of vector databases like Amazon OpenSearch Serverless, both directly and through Knowledge Bases for Amazon Bedrock, to implement effective personalization strategies for their digital front door solutions. By applying metadata filters during the retrieval process, the large language model can retrieve and summarize information tailored to each member’s specific enrolled plan, ensuring accurate and personalized responses.

Implementing personalization through vector databases not only enhances the member experience but also mitigates the risk of providing incorrect information, which can have significant consequences in the healthcare industry. By delivering accurate and personalized information, payors can improve member satisfaction, increase operational efficiency, and ultimately contribute to better healthcare outcomes.

As healthcare organizations continue to embrace digital transformation, personalization will become increasingly crucial in creating frictionless and engaging experiences for members. The techniques outlined in this blog provide a solid foundation for payors to explore and implement vector database-driven personalization strategies that align with their specific requirements and use cases.

However, personalization is not a one-time endeavor; it requires a continuous commitment to innovation and improvement. By embracing a culture of continuous learning, leveraging the latest technologies, and prioritizing privacy and compliance, healthcare payors can stay ahead of evolving member expectations and deliver exceptional healthcare experiences.

At AWS, we are committed to driving innovation in the healthcare and life sciences sectors. Our cloud-based services and solutions empower organizations to unlock the full potential of their data, enabling personalized experiences, streamlined operations, and ultimately, better patient outcomes.

Contact Amazon Web Services today to learn how we can collaborate and innovate together, shaping the future of personalized healthcare experiences.