Vijay Chinta

🚨 Medicaid Information Technology Architecture 4.0 Is Coming: Are You Ready for the Next Wave of Medicaid Modernization? 🏛️CMS is modernizing the Medicaid Information Technology Architecture (MITA) framework—and MITA 4.0 is shaping up to be a game-changer. 🔍 What’s Changing? MITA 4.0 moves beyond static frameworks to embrace: TOGAF-based architecture modeling Modular, service-oriented design Built-in FHIR support & interoperability standards Streamlined APD templates for faster CMS approvals Automated tools to reduce state burden 🆚 MITA 3.0 vs. 4.0? The shift is real: ❌ From PDF-based self-assessments to 🔄 automated maturity alignment ❌ From vague standards to 🔧 specific FHIR profiles & API guidance ❌ From custom approaches to 📋 standardized funding templates (e.g., MES APD, OAPD, AOA) 🗓️ When? Key working groups kicked off in 2023. Drafts were shared at MESC 2024. Full release likely by late 2024 or 2025. 🛠️ New Mandates Coming Expect FHIR-based prior authorization, modularization requirements, and seven CMS-standard APD artifacts. The goal: reduce cost and duplication, and build reusable components across Medicaid Enterprise Systems. 🔗 Where to Learn More? MITA GitHub: cmsgov.github.io CMS & Medicaid.gov BerryDunn blog: Helpful updates on APD templates and what states need to prepare Or email: MITAGovBoard@cms.hhs.gov 📣 Bottom Line: MITA 4.0 isn’t just an update—it’s a strategic pivot. States that align early will see faster funding, better interoperability, and a clearer path to transformation. #MITA4 #MedicaidModernization #CMS #DigitalHealth #FHIR #TOGAF #EnterpriseArchitecture #StateIT #HealthIT #Interoperability #APD #GovernmentTech #PublicSectorInnovation #HHS #DepartmentofHumanServices #CAliforniaDHCS #MES #GovTech #PublicSectorInnovation #DigitalGovernment #StateIT #CloudForGood #CIO #HealthEquity Chief Architect Network Chief AI Officer Exchange Chief Architect Forum National Association of State Chief Information Officers (NASCIO)

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Such an exciting year ahead. Healthcare AI is finally crossing from digitization to cognition shifting value from administrative drag to clinical and scientific productivity, with revenue cycle intelligence as the first scalable proof of ROI. Today, administrative functions consume roughly one-quarter of total U.S. healthcare spending making AI-driven cognitive automation one of the few levers capable of reallocating dollars back to care, not just cutting costs. With average hospital operating margins near 2%, even a 30–50 basis-point improvement from AI-driven clinical intelligence can translate into a 15–25% relative margin uplift without adding beds, clinicians, or capital spend. #MBA #Healthcare #AI

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Earlier, I used to think — “Prompts? They’re just plain English. What’s there to learn?” But after spending time with Gen AI models and experimenting with hundreds of prompts, I realized something important — 👉 If you master the art of writing prompts, the model takes care of the science behind them. Prompt engineering isn’t just trendy — it’s essential. And leading companies are already acting on it: 💡 IBM is scaling AI upskilling across the enterprise and has pledged to train 2 million workers in generative AI & prompt-engineering skills. (Futurum Group) 💊 Johnson & Johnson has mandated generative-AI training — including prompt engineering — for 56,000+ employees. (Business Insider) The takeaway? 🧠 The ability to craft effective prompts is becoming as fundamental as coding itself. Whether you’re a business leader, HR strategist, or practitioner — it’s time to embed prompt mastery into your team’s DNA. 🔧 Let’s not treat prompt engineering as a one-and-done session, but as a continuous capability to stay future-ready. 🎥 I’ve launched a new video series to help you start — watch the first episode now! https://lnkd.in/gXNmXau7 #PromptEngineering #AIUpskilling #GenAI #FutureOfWork #AITraining

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Our own EVP, Chief Scientist & Head of Enterprise AI Prem Natarajan recently spoke to TechTarget about Capital One’s partnership with the National Science Foundation (NSF), which will help launch five new National AI Research Institutes to drive breakthroughs in high-impact areas across society like material and computational sciences, economics, and mental health. https://bit.ly/4grWY3m

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#AIJourneyWithMaheshRaja post #8 its been a while I wrote on my core ( AI ) Most of you know that like in any organization we at Conscendo paving our way to become an AI driven organization on par with Industrial paradigm shift that is happening, We too feel the pressure of "AI FOMO ( Feelingof Missing Out )." We see the headlines and feel we need to implement something immediately. But rushing in without a roadmap often leads to failed POCs and, more importantly, lot of invalid learning and may be a risk-scoring of breach of customer trust Here are my three main takeaways on how to approach AI the right way: 1. Identify Bottlenecks, 2. Plan for Fool proof Trust Layer 3. Nominate an AI focal point in the organization 1) In my experience with AI, The most successful AI implementations don’t start with the technology; they start with the team on the ground, check "Where and What are our bottlenecks", 2.) If AI team can’t guarantee that our proprietary data won’t be used to train public models, the risk to our brand is too high. 3) Appoint an AI Champion AI isn't just an "IT project." It requires a Single Point of Contact (SPOC) who understands both the business goals and the technical limitations. Without a dedicated champion to navigate the "hallucinations" and refine the outputs, projects often stall at the demo stage. Let’s stop racing to be first and start racing to be the most reliable. I’m curious to hear from my network-are you currently testing AI in your workflows? What has been your biggest hurdle so far? #ArtificialIntelligence #SMB #DigitalTransformation #BusinessStrategy #Leadership #AIAdaption #Salesforce #ConscendoTechnologies

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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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CIO com has "11 most in-demand AI jobs companies are hiring for Feature - Organizations are optimistic about AI in the workplace, but rapid adoption has sparked the need for new hires to help design, develop, implement, and maintain AI tools and services." https://lnkd.in/eSDdXRcm. #artificialintelligence #aijobs #hiring #mostindemand #ciocom

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Brilliant piece by Will Larson - https://lnkd.in/gkyRE-Q8 I find myself frequently describing LLMs as just another component in our toolkit, and agents as just standard software wrapped around API calls to LLMs that may or may not have access to “tools” - yup, more standard software. Of course, as this is all just software, you can combine it to your heart’s content, creating larger software systems including ones with an agent as the main orchestrator. The compounding non-determinism of systems like these will either feel like magic or a nightmare. Once you see it that way, it’s much easier to reason about.

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AI Is Not Coming for Your Job—But Someone Using AI Might Be We’re at a pivotal moment in the evolution of artificial intelligence. For the first time, advanced models like GPT-4, Claude, and Gemini are no longer just tools for researchers—they’re assistants for writers, analysts, coders, designers, and decision-makers. The narrative that "AI will replace humans" is outdated. The more accurate story is: AI will amplify those who are ready to work with it. Here’s what that means: AI is a force multiplier: It’s not replacing creativity, empathy, or critical thinking—it’s enhancing them. A junior marketer using AI can produce strategy decks, analyze campaigns, and write at the level of someone years ahead in experience. A solo founder with AI can punch above their weight, building prototypes, websites, and customer support flows that used to take a team. The new literacy is prompt literacy: Knowing how to speak the language of AI—how to prompt clearly, iterate rapidly, and refine outputs—is becoming as important as knowing how to use a spreadsheet or write an email. It's a skill that cuts across industries. There’s a growing gap between the curious and the complacent: Those experimenting with AI are discovering shortcuts, new ways of thinking, and entire workflows they didn’t know were possible. Those ignoring it may find their skillsets quietly becoming obsolete. Human + AI > AI alone: AI doesn't understand context, emotion, or consequence like we do. It’s a pattern engine, not a moral compass. The most powerful outcomes still require human insight, judgment, and intent. The question isn't whether AI will change your job. It’s how you’ll change with AI. Start small. Play. Experiment. Build. The future belongs to those who are willing to collaborate—with machines, and with each other.

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Google's algorithms know me very well, as my news feed is filled with articles on Generative AI. The daily buzz around "AI Agents"—specialized bots for every conceivable task—has reached a fever pitch. However, I view "Agent Sprawl" as merely the latest form of technical debt. The future does not lie in managing a library of thousands of static agents, each tailored for specific tasks, but in nurturing a single Orchestrator LLM. While we may not be there yet, that day is on the horizon. By equipping a capable model with our business context and process knowledge, it can dynamically generate the solutions we need in real time. I recently demonstrated this by condensing what traditionally took three months for a proof of concept into just one afternoon of prompt engineering using the Antigravity IDE and Gemini 3. It is remarkable how straightforward what was once complex has become. The models are incredibly capable even today when provided with the right information. Whether designing intricate workflows or automating a Microsoft Teams message notification, the focus should become less and less on hard-coding the "how" into an agent. Instead, we should aim to provide the LLM with the context, including business processes, it needs to solve our problems for us. I get it. We are just learning to crawl and agents are a logical step. Soon though, we will be able to run. It is time to begin the shift from rigid agent management to dynamic, context-aware orchestration, making this vision a reality sooner rather than later.

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