AWS on AI, machine learning, interoperability improving patient outcomes

As the country moves toward value-based care, artificial intelligence and machine learning – paired with data interoperability – have the potential to improve patient outcomes while driving operational efficiency to lower the overall cost of care. By enabling interoperability securely and supporting healthcare providers with predictive machine learning models and insights afforded by genomic research, clinicians will be able to seamlessly forecast clinical events – such as strokes, cancer or heart attacks – and intervene early with personalized care and access to curated information to support a superior patient experience. Further powering these predictive capabilities with location-agnostic, voice-enabled, accessible modalities of providing care advances the practice of medicine to align with what is most convenient, affordable and targeted for the specific needs of patients. Healthcare IT News sat down with Phoebe Yang, general manager for non-profit healthcare at Amazon Web Services, to discuss these subjects, offering healthcare CIOs and other health IT leaders lessons in state-of-the-art technologies. How can AI and machine learning combined with data interoperability enhance patient outcomes and operational efficiency to lower care costs? A. Interoperability among medical information systems is foundational – or should be – because without it, physicians don't have ready access to their patients' complete medical histories.