Kedar Ganta

Most health plans are well into their interoperability journey.  But the hardest part is not FHIR or APIs. It is whether data is governed, reusable, and ready to support exchange at scale.  Interoperability requires data to be intra-operable first.  Abacus Insights’ Vincent P. Tumminello shares his thoughts on Three Transformation Paths to CMS-0057 Compliance. Explore more here: https://hubs.ly/Q043hgRZ0 #FHIRAPIs #CMS0057 #Interoperability #CMSInteroperability #HealthTech

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The finalized 2026 CMS Rule introduces increased complexity across MIPS and ACO reporting from new interoperability standards to the transition toward digital quality measurement via FHIR-based architectures. For healthcare organizations that are already stretched thin, these shifts could mean missed quality opportunities, misaligned systems, or worse, unexpected penalties from data and process gaps. This webinar from ReportingMD offers a roadmap to mitigate risk and maximize readiness. Miranda Stork, SVP of Product and Analytics, and Dr. Larry Blosser, CMO, will guide attendees through the finalized provisions, explain what’s actionable now, and share how provider feedback influenced CMS decisions. Key takeaways will include: -The regulatory shifts that will most affect your organization’s quality reporting strategy and composite scoring -How to safeguard your outcomes by aligning workflows, data infrastructure, and interoperability efforts now -What operational and technical adjustments do you need to make, particularly around dQMs, real-time data feeds, and standardized API integration, before the next submission window opens If you're leading compliance, performance analytics, or regulatory programs, this session will help you avoid downstream disruptions and move into 2026 with greater control and clarity: https://hubs.ly/Q03Txm6M0

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AI and EHRs are no longer separate stories — they’re merging into a single ecosystem that unites documentation, guidance, and revenue integrity. Dr. Puneet Sahni, MD, MHA, Specialist HSA, FACHDM explores how this convergence is reshaping care at every scale. https://lnkd.in/eBrs2wHn #HealthDataManagement #AIinHealthcare #EHRInteroperability #DigitalHealth #HealthcareInnovation

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HEALTHCARE AI IN 2026 1. Healthcare AI has shifted from answering questions to completing end‑to‑end tasks: Autonomous agents now manage full workflows across EHRs, payer portals, and scheduling systems—handling major operational loads like half of patient call volume or preparing tumor board packets by pulling charts, staging cancers, and drafting treatment plans. This represents the biggest shift since the EHR. 2. Data—not algorithms—is the real limitation: Despite rapid model improvements, most healthcare data remains fragmented. Without clean, structured data, AI cannot take meaningful action. 3. Epic is building a dominant predictive intelligence layer: With 300 million patient records and its Comet model trained on 151 billion tokens, Epic aims to define the predictive backbone of U.S. healthcare. Comet forecasts readmissions, cardiac events, and disease trajectories across dozens of clinical tasks, positioning Epic as the platform others may have to build on. 4. AI startups are moving beyond pilots into real enterprise scale: Heidi Health manages millions of weekly consultations, Knowtex secured a major VA documentation contract, and Doctronic gained the first state approval for AI-driven prescription renewals. These are operational deployments. 5. OpenAI’s 2026 entry is an inflection point: ChatGPT Health brings consumer data connectivity, while ChatGPT for Healthcare delivers HIPAA‑compliant clinical capabilities through GPT‑5. Early adoption by top health systems signals a shift toward AI marketplaces instead of standalone tools. 6. The emerging frontier is actionable, explainable AI: Healthcare increasingly requires AI that explains why something will happen and recommends actions to improve outcomes. Techniques like graph neural networks and causal inference help manage comorbidities, reduce bias, and build clinical trust. 7. Data portability is becoming more important than interoperability: Portability empowers patients to move their data across systems and AI services. With TEFCA expanding nationwide exchange, companies like Function Health are enabling consumers to bring their data and receive personalized insights—reshaping patient engagement. 8. AI is expanding into financial operations: Beyond clinical tasks, AI now supports plan selection, utilization optimization, value‑based care performance, and risk modeling, essential as capitated payment models grow. 9. Regulation is diverging, creating uneven innovation paths: The FDA continues approving AI devices and enabling faster model updates, but states like Utah are pushing ahead with autonomous prescribing. 10. The strategic takeaway: integration wins: The industry is moving from summarizing information to coordinating actions across fragmented workflows. Organizations that unify data and deploy workflow‑orchestrating AI agents will define the next era of healthcare infrastructure.

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One of the most interesting debates I heard today at HIMSS... “Is it time to trust LLMs over interoperability standards?” For decades, healthcare IT has tried to solve interoperability the same way: Build structured standards → translate data → move it between systems of record. FHIR. HL7. Interfaces. Mapping tables. Endless implementation cycles. But the panel posed a provocative idea: What if LLMs flip that model entirely? Instead of forcing every piece of healthcare data into rigid structure… 👉 Let LLMs work directly with the messy, unstructured reality of healthcare. #RaihanReacts to some themes that stood out: 1. LLMs make massive EHR outputs digestible One panelist joked that they “spent yesterday afternoon in Claude Code building their own apps.” That comment actually reflects a real shift happening across healthcare IT. Clinicians and operators are suddenly able to take: • thousands of lines of chart data • referral notes • discharge summaries • scanned PDFs • messy clinical documentation …and have LLMs summarize, extract, and explain it in seconds. The result: 👉 EHR data becomes understandable and actionable, not just stored. 2. Unstructured data may actually be healthcare’s natural format Healthcare has always struggled to structure everything. But maybe that was the wrong goal. Clinical care is inherently messy: • free-text notes • patient narratives • imaging reports • external records • faxes (still…) LLMs are the first tech that actually thrives in this environment. Instead of forcing the system to change…the tech adapts to healthcare’s complexity. 3. Accuracy and governance are the real challenge Of course, trusting LLMs introduces a critical question: How do we verify the outputs? Several panelists talked about the need for: • monitoring frameworks • validation layers • auditability of summaries • “toggle switches” to turn LLM workflows on or off 👉 This is going to be one of the major operational challenges of AI adoption in healthcare. Not whether models can summarize data. But whether orgs can safely trust those summaries at scale. 4. Early operational wins are already emerging A few examples discussed: • Faster prescription workflows • Referral turnaround improvements • Automated chart summarization for intake and review These are small changes individually. But across a health system, they add up to thousands of hours of operational efficiency. My take: - Interoperability standards are not going away. - Systems of record will still matter. - But LLMs may become the universal interface layer that sits on top of them. HIMSS Global Health Conference & Exhibition

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💡Has anyone tried new feature set of Claude for Healthcare in real workflows yet? Healthcare AI is shifting — and interoperability is the gatekeeper. In mid-January, Anthropic shipped a prior authorization review skill for Claude for Healthcare, complete with #FHIR dev tools, CMS coverage connectors, and HIPAA-ready infrastructure. The signal is clear: real impact happens where AI touches regulated, rules-heavy workflows — not chatbots or note summaries. Prior authorization, claims, and coverage verification are where healthcare actually loses time and money. What made this possible? Deep integration into FHIR-based systems, payer logic, and audit-ready infrastructure — exactly the kind of environment where Meditecs solutions help connect AI to the hospital data layer safely and reliably. At Meditecs, we’ve long believed that interoperability is not a feature — it’s the foundation. In healthcare, the smartest model isn’t enough. Winners will be those who plug AI into the workflows that matter — without breaking compliance or interoperability. ❓ The question is: is your organization ready to let AI touch the workflows that really drive outcomes? #HealthAI #Interoperability #FHIR #Innovation

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Integrated healthcare delivery systems address the inefficiencies of fragmented care through clinical and organizational alignment. This overview details the technical frameworks, FHIR-enabled interoperability, and the transition toward value-based outcomes. Read the Blog: https://lnkd.in/gwcjCF33 #HealthcareIT #IHDS #HealthSystems #Interoperability #DigitalHealth

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Oracle Health's new AI-native EHR is now “available” to ambulatory providers, with their AI hospital EHR coming out in 2026. My 4 thoughts… First, the cliff notes on the announcement: → A push for “voice-first” where clinicians can retrieve patient data by just verbally asking: “What meds is this patient on?” → Smart AI assistants to surface patient summaries, clinical insights, evidence-based guidelines, clinical trial opportunities, etc. → Built on Oracle Cloud Infrastructure (OCI) → “Open” - meaning that health systems can use Oracle’s AI agents, build their own agents or integrate with third parties → Note: Certification is pending final regulatory approval - not yet in production Now onto my 4 thoughts (or speculations): 1️⃣ The No. 1 goal is to motivate health systems to move to Oracle Cloud Infrastructure I don’t know how much Oracle truly cares about the EHR, but they’d love everyone to be on OCI - regardless of what systems sit on top of it. The healthcare cloud market will grow to $200B in 10 years and Oracle wants a big chunk of that. 2️⃣ The No. 2 goal is to maximize customer retention - by being competitive on AI for the clinician UI/UX to minimize churn. Epic’s been testing 100+ AI use cases in the EHR with health systems over the past year, so just to stay in lock step, Oracle needed to release something beyond the Digital Assistant and add more clinical value. 3️⃣ Voice has so many exciting use cases, but it’s not going to be enough to leap frog the competition. I think many people actually underestimate how much faster they could operate if they did things more by voice and less by text. Many of us don’t do “voice messages” or use the mic to write texts… but as someone who has started to try it, it’s WAY faster. For Oracle to make voice a killer feature, they not only have to make the tool available, they have to truly evangelize its use. BUT… it’s going to be table stakes. They need something much bigger to win back customers, especially the big AMCs. 4️⃣ Despite the messaging around an “open” system, I suspect the push for Oracle’s own AI assistants / agents will be prioritized over making it third party friendly - BUT I think flipping this and demonstrating true openness is an advantage Oracle should grab. There’s a reason why third party AI scribes have flourished with Epic health systems, and I can’t name a single health system on Oracle that’s using one of those third parties (I’m sure there are exceptions but they are clearly rare) So I think health systems will want Oracle Health to prove the openness is truly there. And they SHOULD… because that’s a way to compete with Epic whose own customers often raise concerns isn’t as open as it should be. The problem, of course, is that being “open” and maintaining so many integrations with third parties is a LOT of work… fair. Providing their own MCP endpoint and making it easier for third-party Agents to connect makes this possible. What do you think?

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