Category: AWS HealthLake

Healthcare organizations are being asked to widen access, protect increasingly thin margins, and keep clinicians from burning out. Telehealth is one of the most effective levers on all three, and it’s no longer a pandemic stopgap: 71.4 percent of physicians now use it weekly, nearly triple the pre-pandemic rate, and 72 percent of hospitals offer at least one telemedicine service (AMA, 2024). Adoption varies widely by specialty, from 68 percent of psychiatrists conducting more than a fifth of visits virtually to under 5 percent for surgical specialties, and behavioral health accounts for roughly two-thirds of telehealth claims nationally (FAIR Health, 2024), with primary care and chronic disease management as the next growth frontier. But telehealth’s promise is undercut by a growing burden: documentation is now the leading driver of clinician burnout, and that’s where agentic AI demonstrates its value.

In this post, we show how you can use AWS HealthLake to meet CMS-0057-F requirements while building a FHIR data solution that accelerates your broader digital transformation.

We are excited to announce resource matching for AWS HealthLake, now available in gated preview. Resource matching is a built-in capability that automatically identifies FHIR resources representing the same real-world entity and links them without modifying or merging the original data.

In this post, we build on that foundation by constructing specialized AI agents for each data modality along with a supervisor agent that orchestrates cross-modal analysis using Amazon Bedrock AgentCore and Strands Agents SDK. We also train predictive AI models with Amazon SageMaker AI to predict patient outcomes from multimodal features. To further explore the implementation details and get hands-on experience, refer to the accompanying code repository.

In this two-part blog series, we show how you can build agents that interact with multimodal HCLS data, making it easier for end users to query, explore, and ask questions of the data. We build on previous guidance for multimodal data analysis, which demonstrates how to store, query, and analyze clinical, genomic, and medical imaging data using purpose-built AWS services.

In this post, we describe a comprehensive, HIPAA-ready generative AI architecture for healthcare on Amazon Web Services (AWS) using a defense-in-depth approach. By layering compliance controls at multiple distinct levels, this architecture creates a system where no single point of failure compromises patient data protection, and each component that touches ePHI is independently auditable.

In this blog, you will gain detailed insight into the scientific rigor behind Amazon Connect Health, including our training methods (supervised fine-tuning, reinforcement learning, multi-agent collaboration), how we validate against real-world data (automated and manual evaluation, LLM agentic environments), and the evidence mapping and source tracing we provide for every output to reinforce safety and trust.

This blog post explains the vision behind how healthcare organizations can move from fragmented data to a connected, AI-ready future with AWS HealthLake

In this blog, learn how Electronic Medical Record (EMR) systems on AWS can evolve from static clinical repositories into the foundation for generative AI in healthcare—through two complementary strategies that extend existing systems with AI services or build on open standards like FHIR and openEHR.

In this post, we show how EHR companies, healthcare independent software vendors (ISVs), and tech-enabled provider organizations can use Amazon Connect Health to embed these capabilities into their existing workflows using a single, unified Software Development Kit (SDK).