AWS for Industries
Author: Chris Haddad
Chris Haddad is a Senior AI/ML Solutions Architect in the Global Healthcare and Life Sciences team at Amazon Web Services. He is a results-driven and passionate machine learning specialist with over eight years of experience in the healthcare and life science industries. He leverages his expertise to help customers solve complex problems and achieve their business goals through the innovative use of artificial intelligence and machine learning.
Using Amazon Q in healthcare organizations
Healthcare organizations are constantly seeking innovative ways to enhance patient experiences, boost developer productivity, and streamline processes. The advent of Generative AI (Gen AI) has opened up a world of possibilities, allowing healthcare providers to harness the power of advanced language models and unlock new realms of efficiency and innovation. Amazon Q, available as Amazon […]
Revolutionizing Real-World Evidence: How Generative AI Can Simplify Data Exploration
Real-World Evidence (RWE) is a domain in which new medical evidence is generated based on data from healthcare and other health services from “the Real World”, compared to clinical trials which are controlled and from specific medical evidence generation. This domain is an evolution of HEOR (Health Economic and Outcome Research) and is particularly inspired by […]
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 […]
Generative AI-Powered Clinical Intelligence: Safely Driving Better Outcomes
Healthcare organizations face immense challenges in gaining insights from the vast amounts of unstructured patient data they collect, since about 80% of medical data remains unstructured and unused after it is created (Kong, 2019, NIH). Clinical notes written by doctors and nurses are a prime example; they contain a wealth of information about patient conditions, […]
Interact Conversationally with AWS HealthLake
Large language models (LLMs) are revolutionizing the way we interact with technology, and AWS HealthLake is no exception. HealthLake is a secure, HIPAA-eligible data lake that allows healthcare organizations to store, transform, and analyze their health data at scale. By combining HealthLake with an LLM, healthcare providers can interact conversationally with their data, gaining insights and making decisions […]