Imran Chaudhri

Insourcing PI with AMS: two engines, one platform, compounding savings. 1) PredictPi - PI nanoscripts plus governance (platform savings): • $70+ PMPY incremental savings directly attributed to AMS software • $400M+ verified annual savings at scale (anonymized national TPA/ASO) • ~90% faster flag → decision; 30-40% higher audit precision • 250+ users unified across PI/SIU/Claims with transparent, rules-based evidence 2) AI-Bill - automated Itemized Bill review (IBR) savings: • Micro-sprint result: ~$7.1M identified in ~12–12.5 hours • >$9.4K–$9.8K per minute in savings; >$0.57M–$0.59M per hour • >11,000× ROI on labor (at ~$50/hr), yes, empirical data and metrics make this attainable. Together (additive impact with one Health Affordability Platform): • PredictPi’s continuous surveillance + nanoscripts deliver broad, recurring PMPY lift across lines of business. • AI-Bill’s high-throughput IBR adds concentrated, rapid savings bursts, without adding headcount. • Subscription model democratizes PI (predictable TCO, aligned with prevention and fiduciary governance) while keeping contingency only for niche retros/capacity spikes. Why it works: • Aligned incentives: prevention + unit-cost reduction, not just recoveries. • Capability transfer: tools + reference directories + nanoscripts move IP in-house. • Operating discipline: governance layer, evidence packs, provider-ready documentation, and enterprise-wide queueing for 200–300 users. The punchline: • Platform (PredictPi): $70+ PMPY; $400M+ verified annually; speed and precision gains. • IBR (AI-Bill): $7.1M in hours; >11,000× labor ROI. • Model: subscription pricing that lowers total cost, scales savings, and strengthens fiduciary posture. Call to action: If you want to insource PI, cut TCO, and run a continuous prevention engine-with rapid IBR savings on top—let’s connect. Summary – You Will Want to Save This Post PredictPi drives recurring PMPY + governance at scale; AI-Bill adds rapid IBR wins. One subscription platform; additive savings; stronger fiduciary confidence. Amspredict.com Pricetransparency.com #PaymentIntegrity #PriceTransparency #ERISA #HealthcareAnalytics #Claims #SIU #TPA #SelfFunded #Automation #Affordability #FiduciaryRisk

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Excited to share the latest news on the joint work Microsoft has done with Paige on a multimodal pathology foundation model for precision medicine applications.

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Big news in neurotech: Sam Altman has launched Merge Labs, a bold rival to Neuralink. The battle for brain-AI interfaces is heating up - and the future of human augmentation just got more competitive. ⚡ Read more: https://infoproweekly.com/ #AI #SamAltman #Neurotechnology #BrainComputerInterface #MergeLabs #TechInnovation #FutureOfWork #Neuralink #DeepTech #B2BTech #InfoProWeekly

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AI Visibility Blueprint™ is an engineering manual for building AI-readable digital infrastructure using canonical identity architecture, graph systems, and retrieval confidence design. This workbook-style manual is a structured execution manual designed for professionals who must engineer AI-readable digital infrastructure rather than merely optimize content for discoverability. This manual represents the Execution and Infrastructure Layer within the AI Discoverability Architecture and Retrieval Systems™ Series, and it assumes prior conceptual familiarity with AI visibility frameworks. This is not a SEO visibility guide. It is a build manual for structural discoverability systems that AI retrieval engines can resolve without ambiguity. #AIVisibility #AIera #AI2026+ #Beyond2026 #AIInfrastructure #AIDiscoverability #AISEO #VisibleToAI #ConsciousVisibility https://lnkd.in/g9URpmgG

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Fragmented data is one of healthcare’s biggest challenges. Siloed systems limit visibility into patient needs, care patterns, and financial performance. An integrated data layer—or "Health Fabric"—helps unify disparate sources into a shared intelligence framework. This enables: - Real-time decision-making - Better alignment across care settings - A stronger foundation for value-based programs True transformation starts with connected insight. #Interoperability #DataIntegration #HealthIT #MedeAnalytics

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Having seen the data and years of research behind Oxon2, I can confidently say it deserves urgent, widespread adoption. Cutting PM2.5 emissions by up to 90% translates to healthier people, cleaner communities, fewer sick days, and meaningful cost savings. This breakthrough isn’t just cleaner fuel technology; it’s a strategic advantage transforming health, productivity, and sustainability all at once. Fewer machine interruptions mean more uptime. Better fuel efficiency lowers costs. Engines run optimally longer, even in tough conditions. The question isn’t if Oxon2 should be in every tank, it’s which leaders have the vision to seize this opportunity now and lead their industries into a cleaner, more efficient future.

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AI-based solutions deliver the greatest value when they reflect real-world clinical complexity — not one-size-fits-all assumptions. Access to timely, organized, clinically relevant, specialty-specific patient records that align with existing workflows matters. That feedback helped shape the evolution of our AI-based eHealth Connect® healthcare interoperability platform into three flexible tiers —eHealth Connect® On-Demand, eHealth Connect® Select, and eHealth Connect® Complete — designed to meet organizations where they are, from rapid access to and organization of digitally available patient records to comprehensive retrieval, clinical organization, and expert human support. Learn more about eHealth Connect®: https://hubs.la/Q042Js7R0

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TOON vs JSON: The Modern Data Format Showdown Built for the AI Era The way we structure and exchange data is evolving faster than ever, driven by the rise of Large Language Models (#LLMs). While #JSON (JavaScript Object Notation) has been the undisputed champion for over a decade, a new, purpose-built format called #TOON (Token-Oriented Object Notation) is emerging as the preferred choice for #GenAI developers. https://lnkd.in/duU9yzTe

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