Raj J.

New from Packt Publishing >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" by Ravindranatha Anthapu and Siddhant Agarwal Available at https://amzn.to/4l9lLKO This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy. 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Design, populate, and integrate a Neo4j knowledge graph with RAG 🔵Model data for knowledge graphs 🟢Integrate AI-powered search to enhance knowledge exploration 🟠Maintain and monitor your AI search application with Haystack 🔵Use LangChain4j and Spring AI for recommendations and personalization 🟢Seamlessly deploy your applications to Google Cloud Platform

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🚀 Excited to announce the open-source release of Parrotlet-E and the Eka-IndicMTEB dataset. In digital healthcare, interoperability is mission-critical. While excellent work has been done in curating ontologies like SNOMED-CT and formats like FHIR, a significant challenge remains: converting unstructured information from doctor-patient interactions into clinical entities—especially across India's diverse languages and dialects. Parrotlet-E addresses this gap. It's a medical entity embedding model fine-tuned for 10+ Indian languages, enabling accurate clinical terminology tagging despite our country's linguistic complexity. This work aligns deeply with eka.care philosophy of making digital health more usable and ensuring that clinical data is truly interoperable and analyzable—building the foundation for next-generation healthcare workflows. Huge complement to Sankalp Gulati and the team for making this happen. Grateful for the support from IndiaAI, National Informatics Centre, MeitY, and Abhishek Singh sir's team in enabling this work. Try the open-source model and datasets at #AIKosh and read about more details in our blog. https://lnkd.in/g2iJjSVH Congratulations to Ajazahmed Turki Rushabh Vasani Vasanth Murukuri Dr.Sanjana Sn Anuj Sable, Sulabh Katiyar, Anushree .rana, Dr Rajshree Badami Deepak Tuli Amit Bharti Bhaskar Chaudhary Neha Jagadeesh Vishal Kumar Tuniki Ajitesh Kaicker Raghunandan gupta Pranjal Pandit Mohit Rajpal Sumit Kumar Nilaksh Kushwah Dr. Neelesh Kapoor Karthick P Nura Almas Lourdes Dinesh Vishwesh Jainkuniya Trilok Yadav Shyamjith Palakkandy Nadeemahmed Totad Divyesh Jivani Sam Dennis and the whole eka.care team. Please CFBR to reach extended digital healthcare ecosystem to grow this together.

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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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Amit Trivedi's key advice and main recommendation is that healthcare leaders cannot build trusted, scalable systems alone - collaboration through industry standards groups and community engagement is essential for success. Here are some Key Discussion Points from the podcast, full video releasing this week: Current State of Interoperability: Amit explains that after 20 years of development, the U.S. has established a strong foundation with 90% + of providers having certified EHRs. The focus has now shifted from basic data exchange capabilities to implementing TEFCA (Trusted Exchange Framework and Common Agreement) for robust "last mile" interoperability. Privacy vs. Interoperability: Challenging the common perception that privacy and interoperability are in conflict, Amit argues they are complementary forces essential for "responsible data stewardship." He emphasizes that true interoperability requires trust between data-sharing partners. Patient-Centric Challenges: The discussion highlights how patients often lack understanding of what they're consenting to and where their data goes once shared. Amit advocates for more transparent, granular consent mechanisms and better patient control over sensitive data. Technical Evolution: The conversation covers the progression from document-based sharing to granular data elements through FHIR (Fast Healthcare Interoperability Resources), which enables more precise privacy controls and metadata tagging. Certification Insights: Drawing from his 15+ years in testing and certification, Amit explains the evolution from voluntary marketing tools to mandatory requirements, emphasizing that maintaining certification can be harder than initially achieving it. Future Considerations: The discussion touches on next-generation privacy guardrails including zero-trust architectures, metadata tagging, and audit trails, while acknowledging the resource constraints many healthcare organizations face. #TechVariable #Interoperability #EHRintergration #TEFCA #FHIR

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It is applaudable for an Indian AI Healthcare company, DeepTek led by Dr Amit Kharat to receive WHO recommendation for its AI-based SaMD for #TB #screening. It helps reduce the cost of identifying TB cases by half, and strengthens healthcare economics, but also enhances quality of life by enabling timely triaging, even in the subclinical stages of the disease. DeepTek solution has regulatory clearance from both the US #FDA and EU #CE #MDR. In addition to identifying #tuberculosis, the #AI system can detect more than 21 #lung #abnormalities, such as nodules, masses, and pneumothorax. It can also interpret chest X-rays for children aged four and above, enabling its use across broader public health and routine screening contexts. #Healthcare #DigitalHealthcare #AI #India #indigenous Digitalhealthnews digital health news https://lnkd.in/gitpDQKU

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The Promise and Fear of AI Improving Healthcare Diagnostics Imagine a world where AI diagnoses diseases with unprecedented speed and accuracy, transforming patient care. This future is rapidly approaching. The convergence of AI, Web3, and Biotech promises a 100x improvement in healthcare diagnostics, ushering in the Intelligence Age. Yet, this leap forward raises crucial ethical questions. How do we navigate the anxieties and dilemmas that accompany such profound change? Elevate Health 2050 is a community dedicated to guiding and shaping these advancements responsibly. AI's potential is immense. It can analyze vast datasets in seconds, personalize medicine with precision, and democratize access to advanced diagnostics in underserved areas. Picture AI detecting diseases at their earliest stages, enabling timely interventions and proactive care. This isn't science fiction; it's the exponential leap forward we're witnessing. However, progress casts shadows. Job displacement, data privacy, algorithmic bias, and overreliance are real concerns. We must evolve healthcare roles through retraining, safeguard patient data with robust measures, and ensure AI algorithms are transparent and equitable. Maintaining human oversight is critical. These challenges, though daunting, are not insurmountable. Elevate Health 2050 is built on the core hypothesis that the Intelligence Age, driven by Web3, AI, and Biotech, will reshape our world. We champion shared health values and foundational beliefs, guiding ethical innovation. Our community empowers builders—Founders, Experts, Allocators, and Orchestrators—to create patient-centric solutions that ensure equitable access. We believe in a future where health is a human right, sustainably funded and aligned with optimized resources. Join us. AI's transformative potential is undeniable, but proactive ethical considerations are essential. Engage in the conversation. Become part of the Elevate Health 2050 community, and let’s build a healthier, more equitable future together. #EH2050 https://bit.ly/4k3wLco

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India just released the most comprehensive AI healthcare governance framework I've seen. Here is what #SAHI means for you. SAHI - Strategy for Artificial Intelligence in Healthcare for India - is a national governance roadmap that directly addresses the gap most of us face daily: 👉 Deploying AI tools in clinical settings without clear ethical guardrails, accountability structures, or integration standards. Here's what makes SAHI different from previous frameworks: 1️⃣ It mandates consent-based data use from day one Not as an afterthought. Not as a checkbox. As a foundational requirement before any AI system touches patient data. For those of us running AI clinical decision support tools in rural facilities, this matters enormously. Our communities have deep, legitimate concerns about data sovereignty. SAHI gives us a policy backbone to point to. 2️⃣ It requires interoperability with ABDM infrastructure This is the detail most analysts are missing. Any AI solution operating in India's health system must now integrate with the Ayushman Bharat Digital Mission architecture. That single requirement will reshape which vendors survive and which don't. 3️⃣ It creates multi-stakeholder governance. SAHI explicitly pulls in academic institutions, startups, and industry alongside government bodies. This is significant. Top-down health AI governance has failed in every context I've studied. Shared accountability has a better track record. Now pair SAHI with #BODH, the Benchmarking Open Data Platform for Health AI developed by IIT Kanpur and the National Health Authority. 👨‍⚖️ BODH is the enforcement mechanism SAHI needs. Before any AI tool scales across India's health system, BODH will evaluate it against real-world anonymized datasets for accuracy, clinical relevance, and algorithmic bias. It creates standardized national benchmarks so we're not comparing tools using incompatible metrics. Think of it this way: - SAHI sets the rules. - BODH tests whether you're actually following them. Why does this matter for social impact organizations specifically? 🙋 Because most of us are caught between: - Donors wanting AI innovation. - Communities wanting trust. - Governments wanting evidence. SAHI and BODH, together, create the governance architecture that lets us satisfy all three without compromising any. The question I'm sitting with: will state governments adopt SAHI uniformly, or will we see implementation fragmentation? 👇 If you're implementing health AI in India right now, what's your biggest governance gap? Drop it in the comments. 🙋‍♀️ Bonus: I have the final SAHI PDF (unavailable outside India) and an 18-page analysis of what SAHI means for digital health practitioners. Ask for which one you want in the comments.

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𝟭𝘀𝘁 𝘁𝗶𝗺𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 𝗧𝗲𝗰𝗵 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀: → Beg EHR vendors for integration → Listen to investors and advisors → Measure success by venture capital dollars raised → Bend over backwards and do whatever health systems want → Believe clinical study results are what will drive health systems to adopt → Sell at every opportunity when they meet with health systems 𝟮𝗻𝗱 𝘁𝗶𝗺𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 𝗧𝗲𝗰𝗵 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀: → Deliver significant value to health systems, so that health systems demand EHR vendors to integrate → Listen to the market (i.e. healthcare customers) → Measure success by health outcomes delivered and sustainable economics → Act as direct, transparent partners with health systems - even if that means disagreeing → Realize clinical study results are used to justify an emotional decision to adopt that’s already been made → Act as a consultant to help health systems buy the right things What else are you doing differently the second time around?

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The acceleration of AI and blockchain in healthcare is operational with quick transformation across healthcare and life sciences. Mohan Venkataraman shares bold predictions in the BHTY 2026 Healthcare Predictions article at DOI: https://lnkd.in/e6yRqC9C Here’s what’s coming: 🔹 Agentic AI transforming legacy systems Pharma supply chains, revenue cycle management, payer operations, and personalized insurance models are shifting from traditional BPM and middleware to generative AI driven orchestration. 🔹 Smaller, domain specific models Healthcare is moving beyond bulky general purpose LLMs toward precision trained, specialized models designed for clinical, regulatory, and operational use cases. 🔹 Trust + Intelligence + Compliance Blockchain infrastructure combined with AI is aligning with HIPAA, FedRAMP, SOC 1, and SOC 2 standards — making compliance a catalyst for innovation, not a constraint. 🔹 Consumer facing AI health tools From personalized wellness assistants to care plan managers and intelligent chat tools, AI and blockchain are entering everyday healthcare experiences. The convergence of decentralized infrastructure and intelligent systems is redefining how healthcare is delivered, managed, and trusted. 👉 Read the full article: https://lnkd.in/e6yRqC9C 👉 Explore the full current issue of BHTY: https://lnkd.in/ejCSc8z7 #AIinHealthcare #BlockchainInHealthcare #DigitalHealth #HealthInnovation #HealthTech #DecentralizedHealth #HealthcareTransformation

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Apollo’s Strategic Restructuring: A Signal for the Future of Indian Healthcare? Apollo Hospitals’ bold move to demerge its digital health and pharmacy assets (Apollo 24|7 & Keimed) into a standalone listed entity marks a significant milestone—not just for the group, but for the broader healthcare ecosystem in India. What does this mean for the industry? This restructuring unlocks hidden value within Apollo’s integrated model, creating specialized, agile units that can independently scale and attract capital.   For peers like Max Healthcare, Narayana Health, Fortis Healthcare, and Medanta, this raises important questions: 1. Should they pursue digital-health-first spin-offs to keep up? 2. Will focused verticals (like diagnostics, retail pharma, digital clinics) outperform integrated giants? 3. Is the Indian healthcare market now mature enough for multi-entity structures like in global markets? Apollo seems to be betting on a decentralized future where capital, growth strategies, and valuation metrics are tuned to the unique dynamics of each vertical—from hospitals to digital care to supply chains. As Camomile Healthcare, we see this as a trendsetting move—one that could redefine how healthcare conglomerates structure themselves in India. #ApolloHospitals #HealthcareTransformation #ValueUnlocking #HealthcareStrategy #IndianHealthcare #HealthTech #CamomileHealthcare #FutureOfHealthcare

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Jan 14, 2026: The Voice of Healthcare in India (Bharat) is changing with new age AI 🇮🇳 AI models like ChatGPT Health and Anthropic Claude AI Health are poised to revolutionize healthcare in India, not just through powerful insights, but by making quality health information accessible to all. What makes this moment truly transformative? > Local language support - With India’s linguistic diversity, delivering health AI that understands and communicates in regional languages and dialects (Kannada, Hindi, Telugu, Bengali, Marathi, Tamil, and more) is critical. > Equitable access - When AI speaks your language, it breaks barriers of literacy, geography, and socio-economics, enabling millions to access reliable health guidance, early diagnosis support, and personalised care pathways. > Empowering practitioners & patients alike - These advanced LLMs augment frontline health workers, help reduce clinical load, and enhance decision-making while maintaining empathetic communication. By pairing cutting-edge AI with culturally aware, local language communication, we can take a significant step toward healthcare for all, making quality health support reachable, understandable, and affordable across India. At ARTPARK Indian Institute of Science (IISc) we have been working on giving agency to the masses through local language integration into digital health technologies. Some of our works can be found here www.artpark.in Raghu Dharmaraju Rohit Satish Nihar Desai If you wish to learn more and explore collaborations in language space - do connect with Dr Rehan Shahed Ministry of Electronics and Information Technology Department of Health and Family Welfare Services - Govt. of Karnataka DST Centre for Policy Research - IISc Bangalore ChatGPT Anthropic Google Microsoft IT BT Department, Karnataka Priyank M Kharge #AIHealth #HealthcareInnovation #DigitalInclusion #India #LocalLanguages #ChatGPTHealth #AnthropicClaudeAIHealth #Bhasini

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AI's Transformative Impact on U.S. Healthcare In the realm of U.S. healthcare, AI is revolutionizing practices in significant ways: - Enhancing efficiency by utilizing AI-driven documentation tools like GPT and ambient scribing, resulting in saving 1-2 hours per provider daily. - Streamlining operations by integrating disparate systems through FHIR-native APIs and AI middleware, facilitating cohesive insights across Electronic Health Records (EHRs). - Ensuring equity and compliance by developing AI systems with bias mitigation strategies, explainable outcomes using SHAP and LIME, and a transparent design approach. - Improving financial processes through predictive billing, AI charge capture, and fraud detection methods, leading to a potential reduction in denials by up to 30%. - Facilitating rapid adoption with modular AI components that seamlessly integrate with renowned platforms such as Epic, Cerner, and cloud-native systems. The integration of AI technologies is reshaping the landscape of healthcare in the United States, offering solutions that enhance operational efficiency, data interoperability, compliance, financial management, and system compatibility. #HealthcareAI #Innovation #HealthTech

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Healthcare moves fast when the pain is obvious. EHRs took seven years with incentives while ambient scribes scaled in two years without subsidies. Providers and payers are building AI labs, which turns the buyer into a builder. Startups win by owning the workflow, delivering turnkey integration, proving ROI fast, and co developing with internal teams. Sell to the C suite, price on outcomes, and show repeatable production rather than pilots that never end. https://lnkd.in/gYxYDnBe

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