“Nitin was my manager at Roku Inc. In a short time I have got to work with Nitin, he made a real impact in my career and helped me with many skills needed to be effective and progress at work. Nitin helped us a lot in hiring in his short time with us and also helped us adding processes and structure to our project. He is somebody who goes the extra mile to support his team. I really recommend Nitin.”
About
I have spent over 25 years building the engineering solutions that powers the modern Big…
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Isha Foundation
- Present 12 years 8 months
Social Services
• Active contributor to the Isha Foundation, a global organization managed by over 10 million volunteers, promoting yoga and meditation. As a daily meditator for over 10 years, I coach and coordinate Bay Area events aimed at spreading mindfulness. For more information, please visit innerengineering.com.
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Josh Mandel, MD
Introducing Banterop: Testbed for Language-First Interoperability :-) We've historically advanced healthcare interoperability by defining structured data models and workflow-anchored API transactions. This approach struggles with the long tail of diverse healthcare interactions (and if we're being honest: also struggles with the center of the distribution). What if, instead of pre-defining every detail, we could enable systems to negotiate requirements and share knowledge to get jobs done in real time? This is the core question behind "Language-First Interoperability" (LFI). At the upcoming HL7 Connectathon in Pittsburgh, Mark Kramer and I are excited to bring this concept into the HL7 community for early-stage, hands-on testing. This track has an exploratory spirit, reminiscent of the early days of FHIR. We're not bringing a finished solution; we're bringing a hypothesis and a set of open questions that we hope to explore with the community. What are the common failure modes of agent-to-agent conversations? How do we ensure reliability? What are the right patterns for grounding agentic interactions in high-quality data and user preferences? We expect challenges and failures, and that's precisely what will make the track a success -- because we will learn from them. To facilitate exploration, I've developed Banterop: an open reference platform and testbed. For background, see this intro video: https://lnkd.in/gfHVBsx4? The platform's scenarios focus on common, information-asymmetric interactions where one agent has deep knowledge of a patient's clinical history and the other holds the rules, policies, or eligibility criteria for a specific process. Think of a clinician's agent interacting with an agent for a payer, a clinical trial, or a public health registry. The goal is to lower the barrier to entry, allowing participants to bring their own technology (whether it's a client or a server, using Agent-to-Agent or Model Context Protocols) and immediately begin testing against a simulated counterpart to see how they negotiate a path to a decision. This is an experiment in building more adaptable and intelligent health IT infrastructure. If you're interested in the intersection of LLMs, interoperability, and real-world healthcare processes, come and roll up your sleeves! ## Learn more and get involved: Connectathon Track Details: https://lnkd.in/g-cnk4U9 Intro to Banterop Platform: https://lnkd.in/giGFgEgJ
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Gabriel Skelton
88% of patient referrals are still booked manually. Only 35% ever become closed-loop referrals today. This is a massive level of inefficiency & referral leakage that is simply accepted as the norm, today. Not for long… Intake, referrals & scheduling are being permanently disrupted by agentic automation technology. Had such a great time Becker's Healthcare in Chicago, discussing the hottest trends within healthcare tech for providers! #healthcare #referrals #rcm
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Vinod Pulluru
How can hospitals, labs, and clinics seamlessly share patient data? The answer lies in HAPI FHIR's 4-table foundation - simpler than you'd expect, more powerful than most realize. The "4-Table Foundation" (All we need to know): > HFJ_RESOURCE - The Master Registry One entry per patient/resource, regardless of updates. Your permanent record card. > HFJ_RES_VER - The Time Machine Every change preserved. Need last year's medication list? It's there. Audit-ready. > HFJ_RES_LINK - The Relationship Web The magic sauce: connects lab results to patients, prescriptions to visits, tests to diagnoses. Without this, you have data chaos. > HFJ_SPIDX_* - The Speed Demons Pre-indexed search tables. Finding "all diabetic patients with HbA1c > 7%" takes milliseconds, not minutes. Why This Architecture Wins: > Complete Patient Views: One query pulls entire medical history > Lightning-Fast Searches: Optimized for healthcare's complex queries > Audit-Ready: Every change tracked for compliance > Relationship-Aware: Preserves clinical context, not just raw data The Flexibility Factor: HAPI FHIR's real strength? While the JPA implementation uses this proven relational approach, the framework is storage-agnostic. Teams can implement custom storage layers for NoSQL databases like MongoDB - storing FHIR resources as native JSON documents while maintaining the same API contracts and search capabilities. The bottom line? Whether you choose relational precision or NoSQL flexibility, HAPI FHIR's architecture makes healthcare interoperability actually work at scale. #HealthTech #FHIR #Healthcare #Interoperability #EngineeringLeadership #HAPIFHIR
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Aryan Mankotia
We’re pleased to announce that Dr. Barkha Khurana has joined NeuroPixelPeak as our Scientific, Clinical & Medical Advisor. With over 16 years of expertise in neuroscience, physiotherapy, and AI-driven neuroinformatics, Dr. Khurana brings a wealth of experience in neuroimaging, electrophysiology, and brain–computer interface research. She will guide our clinical strategy and uphold the scientific rigor behind our neuro-rehabilitation initiatives. At Neuropixelpeak , we’re addressing one of the most critical challenges in healthcare — the global lack of accessible, outcome-based neuro-rehabilitation for patients living with paralysis and post-stroke disabilities. Our AI + EEG + VR platform redefines recovery by enabling thought-driven motion, communication, and emotional reconnection. As we move forward, we are gearing up for our Seed Round and are onboarding hospitals and rehabilitation clinics for our initial pilot rollout before the end of Q3 2026. Dr. Khurana’s leadership will play a vital role in ensuring that every milestone we achieve aligns with the highest standards of clinical excellence and translational neuroscience. #NeuroPixelPeak #NeuroRehabilitation #AI #BCI #BrainComputerInterface #NeuroTechnology #Neuroscience #DigitalHealth #HealthTech #DeepTech #MedTech #Innovation #ClinicalResearch #EEG #VR #Physiotherapy #StrokeRecovery #ParalysisRehab #Neuroplasticity #AssistiveTech #HealthcareInnovation #AIinMedicine #Rehabilitation #StartupIndia #SeedRound #FutureOfHealth #ThoughtDrivenRecovery #NeuroInformatics #ClinicalValidation #ScientificResearch
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David Cardelle RPh
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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Kyle Schilke
Proud of our own Hailey D'Silva for her work co authoring this blog on streamlining medical imaging workflows with Amazon Web Services (AWS) partner - Flywheel on Amazon S3. Healthcare organizations face several critical challenges when leveraging their medical imaging data for machine learning applications. First, siloed data storage creates complexity – medical imaging data typically resides across multiple disconnected repositories within a healthcare system. Second, stringent compliance requirements mean organizations must carefully pre-process their imaging data, implementing robust deidentification, standardization, and harmonization workflows before feeding it into ML models. The sheer scale of medical imaging data compounds these challenges. With hospitals generating an average of 50 petabytes of data annually, of which approximately 80% comprises medical images, organizations need solutions that can handle massive datasets efficiently and securely. Read on below! #awshealthcare #canadianhealthcare
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