Raheel Retiwalla

Top 9 Healthcare Data Exchange Guides for AI Professionals Healthcare data exchange is the backbone of modern medical innovation, enabling the seamless flow of health information across disparate systems and providers. By leveraging standardized protocols like HL7 FHIR for APIs and HL7 v2 for legacy messaging, organizations can break down data silos within EHRs like Epic and Cerner. For AI professionals, mastering these standards is not just a technical requirement—it is a strategic necessity to secure the high-quality, …...

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Just read how Reid Health rolled out ambient generative AI — and the results are hard to ignore: ➡️ 105,000 clinical notes auto-generated ➡️ Used in 19 specialties ➡️ Happier clinicians, less burnout The tool? Nuance DAX Copilot, built on Azure. It listens during visits, drafts notes, and providers just review and sign. Done. So here’s the real question: If it works this well, why isn’t everyone doing it? Because plug-and-play rarely works in healthcare. ❌ Here's what often gets in the way: Integration headaches with existing EHR systems Concerns about data governance and HIPAA compliance IT team bandwidth is often maxed out Existing workflows are highly customized – no “universal fit” Resistance to change: clinicians need real proof it won’t slow them down What actually works? Partnering with a tech team that can: ✅ Tailor to your real workflows ✅ Handle data + compliance ✅ Support the rollout, not just the software ✅ Build on top of what you already have Reid Health did it right — with the right tech, the right partners, and the right setup. That’s why they saw results. Others can too — but only if they invest in more than just the tool. They need implementation muscle behind it. Link to the article in the comments.

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To add to that, Confluent Cloud Network (CCN) routing simplifies private networking for Flink, reusing existing Kafka infrastructure. It helps teams quickly connect secure pipelines across cloud environments. Security is further strengthened with IP Filtering—giving teams greater control over access in hybrid environments and enabling them to confidently deploy AI at scale. With these enhancements, Confluent is not just enabling smarter AI—it’s making it enterprise-ready. Organizations can now unlock: Seamless integration of historical and streaming data Secure, scalable infrastructure for AI agents Faster, context-aware decisions powered by trusted data Agentic AI is here. With Confluent Cloud, it’s ready for the enterprise. https://lnkd.in/gh-dA7qs #ConfluentCloud #ApacheFlink #SnapshotQueries #AgenticAI #RealTimeData #StreamProcessing #DataAnalytics #EnterpriseAI #BatchProcessing #DataStreaming #DigitalTransformation #Flink #AI #MachineLearning #DataSecurity #PrivateNetworking #IPFiltering Follow our LinkedIn page for more such interesting CX expert opinions/ Thought Leader Interviews/ CX Leadership news/ articles/ updates https://lnkd.in/g9eeFFgh

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Strong example of what responsible AI done right actually looks like. This piece by Lisa Schneider highlights why the Team Select and Cuesta Partners Silver Anthem Award matters beyond the trophy. This is not AI theater. It is applied, operational, and designed for real-world adoption in healthcare. A few things that stood out to me: • Data first, always. The AI model succeeded because the data foundations were built correctly first. Dashboards, business data models, and shared definitions delivered value immediately and reduced downstream risk. • Problem-led AI, not tech-led AI. The team resisted the urge to boil the ocean. They focused on a narrow, high-impact use case where early signals could genuinely change patient outcomes. • Interpretability over black boxes. Risk scores alone are useless in clinical environments. Surfacing contributing factors made the model trustworthy and actionable for care teams. • Workflow is the product. Adoption was designed, not assumed. Clear operating models, clinical co-design, and on-site alignment made the difference between a model that exists and one that gets used. This is the kind of AI story Tech leaders should be studying. Not because it is flashy, but because it is repeatable. Build the data muscle. Pick the right problem. Design for humans. Then scale. Awards are nice. Fewer hospitalizations and better patient outcomes are better. Well worth the read to see how AI that actually improves lives!

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Alrajhi Medicine is adopting Oracle Health Foundation EHR and Oracle Fusion Cloud Applications to increase efficiency and drive growth. Learn how this collaboration can promote improved patient care: https://lnkd.in/dqz8r8WS

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Data fabric and data mesh strategies look perfect in the boardroom. But the Monday morning reality for healthcare payers is often much messier. You likely face a different set of daily challenges: ✅ Trying to unify provider and member data that is isolated across legacy mainframes and modern cloud apps. ✅ Navigating "blind spots" where your metadata catalogs don't match the actual data moving through production. ✅ Attempting to solve interoperability internally before you can realistically scale it to your partners. When you move from strategy to execution, the obstacles become clear. "Point-in-time" views are often impossible because historic context is missing. Graph databases suggest "possible" relationships, but you need absolute certainty for credentialing and contracting rules. And true bi-directional change orchestration often forces you into design compromises just to keep systems running. Our e-book explores how the Gaine Health Data Management Platform (Gaine HDMP) acts as the operational core of your data fabric to: ✅ Build longitudinal, governed records that let you easily answer complex historic, regulatory, and operational questions. ✅ Leverage Opt-In Sync, allowing your systems to subscribe only to the changes relevant to their context while maintaining enterprise-wide alignment. ✅ Provide zero-latency access to mastered, context-rich data that powers your operations, analytics, and AI initiatives. If you are rethinking your approach to a healthcare data fabric, this guide covers the practical obstacles you will face and how to navigate them. Download "Understanding the Gaine Health Data Management Platform" here: https://buff.ly/Zv9RnRB

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