Healthcare discovery e – commerce solutions
The landscape of healthcare discovery on e-commerce platforms presents unique challenges that are vastly different from traditional product searches. Unlike straightforward queries for electronics or books, healthcare inquiries involve a complex web of symptoms, conditions, treatments, and services.
This complexity demands an advanced understanding of medical terminology and customer intent. As Amazon has expanded into the healthcare sector, offering services such as Amazon Pharmacy, One Medical, and Health Benefits Connector, it has encountered both exciting opportunities and significant technical challenges in integrating healthcare services into its e-commerce business, especially regarding healthcare discovery, particularly in AI in healthcare, including machine learning healthcare applications, especially regarding healthcare discovery in the context of AI in healthcare, including machine learning healthcare applications. Amazon Health Services (AHS) has tackled these challenges by leveraging Amazon Web Services (AWS) to enhance search discoverability for health-related queries.
By employing machine learning (ML), natural language processing (NLP), and vector search capabilities, AHS has improved its ability to connect customers with relevant healthcare offerings. This sophisticated approach is used daily to help customers find anything from prescription medications to primary care services in the context of AI in healthcare, including machine learning healthcare applications.
The mission of AHS is to transform healthcare access, making it simpler for customers to find, choose, afford, and engage with the services they need to maintain their health.
health search intent understanding
Integrating healthcare services into Amazon’s e-commerce platform presented two primary challenges: understanding health search intent and matching customer queries with the most relevant healthcare products and services. The challenge of understanding health search intent involves discerning the relationships between symptoms, conditions, treatments, and the healthcare services offered by Amazon.
This requires sophisticated query understanding capabilities that can interpret medical terminology and translate it into common search terms accessible to the average user, especially regarding healthcare discovery, including AI in healthcare applications, including machine learning healthcare applications. Additionally, AHS offerings required specialized approaches for search matching. For instance, a customer searching for “back pain treatment” might be looking for over-the – counter medications, prescription drugs, or even virtual physical therapy services.
Traditional search algorithms optimized for physical products were not sufficient to handle these nuanced service-based healthcare offerings, particularly in healthcare discovery, particularly in AI in healthcare, including machine learning healthcare applications. Therefore, AHS developed specialized methods to connect customers with relevant services like primary care or virtual therapy programs.
AI – driven query understanding optimization
To address these challenges, AHS developed a comprehensive solution that integrates ML for query understanding, vector search for product matching, and large language models (LLMs) for relevance optimization. This solution consists of three main components: ① The Query Understanding Pipeline, which uses ML models to identify and classify health-related searches, distinguishing between specific medication queries and broader health condition searches.
② A Product Knowledge Base, which combines existing product metadata with LLM-enhanced health information to create comprehensive product embeddings for semantic search, including healthcare discovery applications, particularly in AI in healthcare, including machine learning healthcare applications.
③ Relevance Optimization, which employs a hybrid approach using both human labeling and LLM-based classification to produce high-quality matches between searches and healthcare offerings. These components are built entirely on AWS services, with Amazon SageMaker powering ML models, Amazon Bedrock providing LLM capabilities, and Amazon EMR and Athena handling data processing, including healthcare discovery applications, particularly in AI in healthcare, especially regarding machine learning healthcare.
Healthcare search customer journey
The technical implementation of AHS’s solution architecture required addressing the unique challenges of healthcare search on Amazon.com. One key aspect was understanding the customer search journey, which ranges from “spearfishing queries”—where customers have a clear product search intent—to broader, exploratory queries.
AHS developed models to serve this full spectrum of healthcare searches, especially regarding healthcare discovery, particularly in AI in healthcare, especially regarding machine learning healthcare. For spearfishing search intent, AHS analyzed anonymized customer data and trained classification models to understand which search keywords lead to engagement with specific Amazon Pharmacy products. This process involved using PySpark on Amazon EMR and Athena to collect and process data at scale.
For broader health search intent, a named entity recognition (NER) model was trained to annotate search keywords at a medical terminology level, using a corpus of health ontology data to identify concepts such as conditions and treatments, particularly in healthcare discovery, particularly in AI in healthcare in the context of machine learning healthcare. This model was further enhanced with LLMs to expand the knowledge base.
healthcare product knowledge base
With the ability to identify health-related searches, AHS needed to build comprehensive knowledge bases for its healthcare products and services. Starting with existing product data, AHS utilized LLMs to layer in additional health conditions and treatment-related keywords.
The result was a significantly expanded product knowledge base, converted into embeddings using Facebook AI Similarity Search (FAISS) in the context of healthcare discovery, particularly in AI in healthcare, including machine learning healthcare applications, particularly in healthcare discovery in the context of AI in healthcare in the context of machine learning healthcare. These embeddings enable efficient similarity searches, allowing accurate mapping of customer queries to relevant healthcare offerings. To support this process, AHS utilized various AWS services, including Amazon S3 for storage and scheduled SageMaker Notebook Jobs for large-scale embedding tasks.
The robust foundation of healthcare product knowledge built through this combination of technologies enables efficient search and matching capabilities on Amazon.com.
Retrieval – Augmented Generation healthcare
A critical component of AHS’s solution was implementing Retrieval-Augmented Generation (RAG) to map health search intent to the most relevant products and services. This advanced technique enhances the ability to retrieve and present information that aligns with customer search intent, thus ensuring that users are connected with the appropriate healthcare options available through Amazon, including AI in healthcare applications, particularly in machine learning healthcare.
In conclusion, Amazon’s innovative approach to transforming healthcare discovery using AI and machine learning underscores the importance of adapting traditional search algorithms to meet the unique demands of healthcare services. By integrating cutting-edge technology and leveraging AWS services, Amazon Health Services has significantly improved healthcare access, making it easier for customers to find and engage with the services they need.
