healthcare ai

  1. Pragmatic AI in Healthcare: Ed Reiner's Journey from EHRs to Dragon Copilot

    Edward Reiner’s career is the human story behind healthcare’s most technocratic conversations: a Stony Brook ’77 alumnus who has watched medicine move from paper charts to enterprise data warehouses and now into the messy, promising world of generative AI. His message is simple and insistently...
  2. Optum Real and Microsoft AI Toolkit Aims to Speed Healthcare Revenue Cycle

    Optum’s Optum Real and Microsoft’s cloud and AI toolkit promise to shave months of friction from the healthcare revenue cycle — but the technical, ethical, and competitive stakes are high, and the pilot results to date are company-reported rather than independently audited. Background: what was...
  3. Dragon Copilot at HIMSS 2026: Unifying Clinical Data with a Single AI Assistant

    Microsoft’s pitch at HIMSS 2026 was blunt and unambiguous: unify fragmented clinical data, simplify the work clinicians actually do, and scale those gains across roles and geographies—using one integrated AI assistant built on Azure and threaded into the Microsoft productivity stack. The new...
  4. Embedded AI in Healthcare: A Pragmatic Path from Data to Decisions

    Edward Reiner’s career maps the three-decade arc of healthcare’s data revolution: from paper records to enterprise data warehouses to the arrival of large language models and generative AI tools that promise to reshape clinical workflows, drug development, and health-system management. His...
  5. 2026 ROI Reckoning: How to Sell Enterprise AI That Moves the P&L

    As the first quarter of 2026 unfolds, the era of AI experimentation in the enterprise has given way to an era of accountability. After three years of pilots, proofs of concept, and vendor sampling, buyers are consolidating their stacks, procurement teams are pruning experiments that never...
  6. Making AI Work: A Practical Playbook to Operationalize Enterprise AI

    MIT Technology Review’s new short series, Making AI Work, lands as a practical counterpoint to the hype: a seven-issue, weekly newsletter that walks readers from real-world case studies to the tooling and — critically — the operational steps organizations need to make AI deliver measurable...
  7. CU Anschutz advances safe clinical AI with PDSQI-9 and Cliniciprompt

    CU Anschutz researchers are moving from proof‑of‑concept to practical deployment, delivering a set of validated, clinician‑centered tools designed to make Large Language Models (LLMs) and other health A.I. safer, auditable and useful at the bedside—efforts that combine peer‑reviewed measurement...