Elias Tharakan

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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Revenue leakage often hides in processes that feel “normal.” Repeated write-offs, routine corrections, and accepted denials quietly erode margins. Without assessment, they become invisible. A financial audit connects losses to operations. ✅ Where write-offs originate ✅ Why denials are recurring ✅ How rework consumes staff time ✅ Where access or documentation failed ✅ What leadership never sees in reports Practices that identify leakage early protect revenue without renegotiating rates or increasing volume. #MedicalPracticeConsultant #GoHealthcarePracticeSolutions #PracticeAudit #RevenueIntegrity #HealthcareOperations #RevenueCycleManagement #HealthcareCompliance #Leadership

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Discover the power of Centaur Data Connectors in our latest blog! 🚀 Say goodbye to custom pipelines and effortlessly integrate complex healthcare data for seamless processing. Our connectors are purpose-built for the healthcare industry, tackling its unique challenges with precision and ease. 🩺 #HealthcareInnovation #DataIntegration #CentaurDataConnectors #HealthChain #Centaur

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Inductive EDC is accelerating the development of life-saving therapies and enhancing patient outcomes. We've also developed new products that help automate protocol creation, utilize AI to a streamline site selection, and more. And regardless of size but especially if you're a small to mid-size pharma, biotech, or CRO interested in cost-effective solutions as you begin to scale up, reach out to learn how we're helping others.

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Becoming Clinical Interoperability This Post discusses the multiple interconnected terms we use when faced with specifying clinical interoperability scenarios. We will introduce clear definitions and relationships between the core concepts of interoperability in a healthcare context. We are going to illustrate the relationship of the concepts through several interoperability workflow patterns rendered by sequence diagrams. We will focus on the following key workflows: 1. Basic FHIR API Workflow: Patient data retrieval via FHIR API. 2. Medical Device Integration Workflow: Infusion pump sending data to EHR. 3. Cross-Enterprise Document Sharing (XDS) Workflow: Sharing a clinical document between two different healthcare organizations. 4. Laboratory Result Delivery Workflow: From lab device to EHR via LIS and HIE. These diagrams depict the flow of data and the interactions between the clinical interoperability core concepts (Platform, System, Component, Device, API-interface, and Artifact) in various interoperability scenarios. Each diagram highlights the role of standards (like HL7, FHIR, IHE profiles) and the importance of APIs and interfaces in enabling these workflows. A System resides on a Platform. It is composed of several Components. It connects to other Systems and Devices via API-interfaces. It creates, consumes, and manages Artifacts. An Artifact is the data object created by a System, Component, or Device. It is transmitted from one entity to another via an API-interface. It is stored and managed on a Platform. In essence, Platforms provide the foundation, Systems perform the core functions, Components and Devices create and consume data, APIs are the communication channels, and Artifacts are the standardized data packages that flow through these channels. All this, to achieve continuity of care with seamless interoperable, and a medical outcome, that improves the citizen's functional health status. This comprehensive explanation of clinical interoperability concepts and workflows, along with conceptual frameworks, standards compliance regulation, and visual representation of conformance rules (IHE Implementation Guides), provides a holistic view of how healthcare systems achieve interoperability in practice. Enjoy! jvilalta@vico.org https://vicoacademy.com/ #VICOOPENAcademy @VICO OPEN Academy 

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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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I am not a fan of AI agents in actual clinical operations for a few reason. I see and understand the take it slow approach, beyond narrow applications. “AI agents are poised to transform healthcare, but current medical device regulations aren't built to handle their autonomy, adaptability, or broad functionality” Jan Beger

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