Category: Healthcare

In this post, we show you how Amazon Health Services (AHS) solved discoverability challenges on Amazon.com search using AWS services such as Amazon SageMaker, Amazon Bedrock, and Amazon EMR. By combining machine learning (ML), natural language processing, and vector search capabilities, we improved our ability to connect customers with relevant healthcare offerings.

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

In this post, we demonstrate how Kyruus Health uses AWS services to build Guide. We show how Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) from leading AI companies and Amazon through a single API, and Amazon OpenSearch Service, a managed search and analytics service, work together to understand everyday language about health concerns and connect members with the right providers.

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

In this post, we demonstrate how to implement this evaluation framework using Amazon Bedrock, compare the performance of different generator models, including Anthropic’s Claude and Amazon Nova on Amazon Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

MSD, a leading pharmaceutical company, collaborates with AWS to implement a powerful text-to-SQL generative AI solution using Amazon Bedrock and Anthropic’s Claude 3.5 Sonnet model. This approach streamlines data extraction from complex healthcare databases like DE-SynPUF, enabling analysts to generate SQL queries from natural language questions. The solution addresses challenges such as coded columns, non-intuitive names, and ambiguous queries, significantly reducing query time and democratizing data access.

In this post, we discuss how you can use AWS HealthScribe with Amazon Q Business to create a chatbot to quickly gain insights into patient clinician conversations.

In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. The solution presented in this post not only enhances the member experience by providing a more intuitive and user-friendly interface, but also has the potential to reduce call volumes and operational costs for healthcare payers and plans.

Generative AI and transformer-based large language models (LLMs) have been in the top headlines recently. These models demonstrate impressive performance in question answering, text summarization, code, and text generation. Today, LLMs are being used in real settings by companies, including the heavily-regulated healthcare and life sciences industry (HCLS). The use cases can range from medical […]