Rethinking the value of AI in healthcare: Time or pause?

For those holding their breath.. here is how we're tackling OCR differently at Alaffia Health: Instead of just converting scanned PDFs into text, we use advanced AI that “sees” and “reads” documents at the same time. Here’s how it works in simple terms: 1). Smart tagging ➡️ Our AI scans thousands of pages and tags only the ones with the information you actually need (e.g., lab results or discharge notes). 2). Focused extraction ➡️ A second AI zooms in on those pages to pull out the exact data points. 3). Fact agents ➡️ Specialized AI agents extract critical facts tailored to your workflows like diagnoses, lab values, medications, treatment plans, and even social determinants of health (!!!🫨). This layered approach makes large-scale medical record reviews faster, more accurate, and highly configurable for any claim or clinical decision-making process. When it comes to complex healthcare data, AI shouldn’t just “read” documents, it should understand them! 🔁

We’ve all used AI to draft emails or plan dinner. But in healthcare, the stakes are higher.    Aya Ideas Retreat keynote speaker Sarah Branfman will take on the unknowns of AI in care delivery. She'll share how to take AI from a promising concept to measurable outcomes with the right tools, invested teams and solutions designed to last. 

In the Healthcare Payer Industry speed without context leads to confusion. Claims may be processed in seconds, but the “why” behind each one often takes 30+ minutes to uncover. We helped transform this bottleneck with a Generative AI Retrieval Engine that makes context instant, transparent, and usable in the tools teams already know.  The result?   ● 25–30% faster claim resolution  ● Context-rich, source-cited answers  ● Fewer disputes and escalations  ● Tuned for real-world healthcare constraints Built with Persistent — clearer claims, smarter teams, stronger outcomes.   Read the full case study: https://lnkd.in/dBNTmymR Aniket Goel, Jaideep Vijay Dhok, Sameer Dixit, Shimona Chadha, Ravindran M. #ReAIlImpactStories #ReAImaginingTheWorld

Doctor vs Data AI in healthcare isn’t “coming.” It’s already here. It knows patterns across millions of cases. It spots what even the sharpest eyes might miss. But here’s the catch: When AI says one thing and your doctor says another, WHO DO YOU TRUST? A doctor brings experience, intuition, and empathy. AI brings scale, speed, and global insight. One speaks from intuition. The other from information. And that’s the dilemma patients are about to face. In the end, trust might not lie in choosing one over the other, it might lie in how they work together.

Our own Kevin Deutsch spoke with Healthcare Innovation about one of the biggest barriers to AI in healthcare — scattered data. His take: Health plans don’t need more hype. They need data governance, centralization, and real-time access to critical information before AI can deliver real value. From chatbots that reduce call center strain to generative AI that accelerates testing, the potential is real. But without unified data, it falls flat. Read more from Kevin’s interview 👉 https://bit.ly/4o6gsOv

Oracle Health and Epic are in the race to see who can make the biggest impact—breaking down silos across clinical, financial, and supply chain. The prize isn’t just market share. It’s transforming how healthcare delivers care, manages costs, and builds resilient operations. And AI will be the accelerator. Looking forward to Seema Verma 's insights at #OHLSSummit on how embedded AI is driving this competition—and the transformation it promises. Thanks Greg Aaron for sharing.

AI-generated answers still struggle with accuracy and trust, especially in critical fields like healthcare. Retrieval-Augmented Generation (RAG) techniques help by grounding AI outputs in real-world data, improving reliability. But these systems often act like black boxes, making it hard to understand how they produce their results. Our new framework, KG-SMILE, brings clarity to RAG by pinpointing which parts of a knowledge graph influence AI-generated responses. This transparency helps balance accuracy with explainability, a vital step for sensitive applications. I believe trustworthy AI requires not only strong performance but also clear explanations that people can follow and trust. How important is transparency to you when using AI in decision-making?

One simple chart shows how AI can make US healthcare faster and more efficient.

Gain actionable insights in our free on-demand webinar, and explore the potential of AI to elevate—not replace—clinical expertise. Key takeaways include: 🧠 What to ask about AI validation: testing protocols, expert input and data sources ✅ How to assess readiness and fit for AI-enabled decision support tools 🚀 Ways to deploy AI that elevate clinical expertise, not replace it ▶️ Register here 👉 https://hubs.la/Q03zLfMB0 #AIinHealthcare #AIinClinicalPractice #ClinicalDecisionSupport #Micromedex

Clients are increasingly asking about AI solutions and support during engagements. AI in healthcare is no longer theoretical. From ambient clinical scribing and revenue cycle automation to smarter supply chain management, success will require a clear strategy.