Real-Time Data Analysis in Health Informatics

⚡ Healthcare Thinks Its Data is AI Ready. Its Not. Its Analytics-Ready. What's the Difference? 🚀 Most health systems believe they're prepared for AI deployment. They have nightly ETLs, enterprise data warehouses, quality dashboards, and retrospective reporting. 📅Traditional data infrastructure was built for reporting. ⚡AI data readiness is built for action. In healthcare, stale data isn't just inconvenient - it's clinical risk. 🤖 What real-time AI actually requires: 1️⃣Data Represents Current System State AI must make real-time clinical decisions based on the most up-to-date information, not outdated snapshots. Is this patient hypotensive right now? Did the troponin just result? Was prior auth denied 4 minutes ago? Snapshot data supports dashboards but cannot support autonomous action. 2️⃣Data is Operationally Derived, Not Operationally Accessed AI systems, especially agentic ones, require data derived from operational systems rather than directly querying them. Direct EHR querying works for BI but destabilizes agentic systems by creating unpredictable load and performance issues that compromise patient safety. 3️⃣Data Preserves Business Meaning as It Changes Data must maintain its clinical and business context throughout the pipeline. Event-driven architecture separates analytics from operations, ensuring semantic continuity so AI agents accurately understand care pathways, authorization status, and workflow state as they evolve. 4️⃣ Data is Event-Driven by Nature Building event-driven capabilities allows AI agents to be automatically triggered by clinical events - streaming vitals, lab results, authorization changes - enabling immediate action without human intervention or latency delays. 💡 Key Insights from Operational Reality: > Latency is now a patient safety variable, not just a technical metric. > Scheduling state awareness enables operational coordination. > Authorization status signals prevent reimbursement delays. > Workflow state tracking reveals patient positioning in care pathways. 📌 My Takeaways AI readiness requires aligning clinical state, workflow state, and reimbursement state in real time through event-driven infrastructure and semantic continuity. Without these four foundational elements, AI agents introduce system strain instead of value. Enterprise AI ROI depends on operational immediacy, not model sophistication. If your AI roadmap is stalling at this layer, let's align it to operational reality. 🥇 This is based on a great webinar hosted by Data Science Connect https://lnkd.in/e6w5G7iv #llm #ai #artificialintelligence #medicalai #healthcareai #genai

What if one of the most important medical decisions of your life came down to five rushed minutes, and incomplete data? In a recent conversation with Fidji Simo, CEO of Applications at OpenAI, she shared a moment that should give every healthcare leader, operator, and technologist pause. While hospitalized, she was about to be given a standard antibiotic for a routine infection. On the surface, it was the correct protocol. But by quickly cross-referencing the drug against her full medical history using AI, she uncovered a critical risk. It could have reactivated a serious past C. diff infection. The physician’s response was telling. “I have five minutes to make rounds. I can’t review years of records.” Modern healthcare is still operating on fragmented data, siloed specialties, and time-constrained decision-making. Even the best clinicians are forced to make high-stakes calls without full context. And this is where the opportunity becomes clear. We are entering a new era where AI is not replacing clinicians, but augmenting their ability to see the whole picture. By connecting longitudinal health data, labs, genomics, wearables, and medical history, we can move from reactive care to truly informed, real-time decision-making. In our full discussion, we explore what this shift means at scale: • Why most clinical errors are not about knowledge gaps, but missing context • How fragmented health systems create unnecessary risk and inefficiency • What it looks like when AI becomes a layer of intelligence across the entire patient journey • So much more This is a systems design problem, a data problem, and ultimately, a leadership problem. The organizations that solve for context, not just care delivery, will define the future of health. Listen to our full conversation here: https://lnkd.in/g_2FsR2q

We don’t run our businesses on lagging indicators. Why are we still managing pregnancy this way? Saturday I had the chance to talk about the intersection of predictive intelligence, wearables, and care delivery with Elizabeth Cherot MD, MBA and Stephanie Rouse from Lucina and Unified Women's Healthcare and Chris Curry from ŌURA. A few takeaways I’m still noodling: 1. Data is only valuable if it changes what happens next. Healthcare has no shortage of dashboards and “insights.” What we need is actionability. If risk is identified but no one intervenes, it’s trivia, not transformation. The opportunity with data is no longer just understanding what’s happening; it’s using that understanding to change what happens next. At Lucina, we use health data to build explainable models so the next best action of care is clear. 2. AI + human care is where magic lives. The best use of technology isn’t removing humans from care. It’s helping the right people show up at the right time with the right information to drive better patient outcomes and better efficiency. 3. The future of maternity care is proactive and real time. Too much of women’s healthcare still waits for a problem to reveal itself. Smart models identify risk earlier, hyper-personalize recommendations, so care teams can act before complications escalate. While claims and EHR data are comprehensive, wearable data gives real-time insights before the visit or before claims drop. 4. Wearables are powerful shared decision-making tools. Continuous data is everywhere and consumers dig it (me, I am consumers 👋 ). When integrated and used appropriately, it may help clinicians understand what their patients are living between visits. This creates better context for shared decision-making. When we can combine the right data with the right care model, we make care more proactive, more personalized, and ultimately more impactful. #WomensHealth #Innovation #AI #Data

Smart Data Infrastructure: The Missing Piece in Value-Based Care Looking through the U.S. Department of Health and Human Services (HHS) AI use case inventory, I was thrilled to see data infrastructure work on the list [1]. I see it as the foundation for everything else. When data flows seamlessly in (near) real-time, amazing things become possible - even without complex predictive algorithms. Like in cooking, quality ingredients often matter more than fancy techniques. Today, I was analyzing the National Syndromic Surveillance Program (NSSP) ED visit data for RSV from the Centers for Disease Control and Prevention (CDC). While the current one week-ish reporting lag isn't bad, I keep thinking about the possibilities with real-time data infrastructure. And I'm not just talking about speed - reliability and consistency are equally crucial. Just like in patient care, being fast only matters if you're also accurate. For Medicare ACOs and MA plans, timely disease surveillance could transform how we work: - Proactively educate care managers about high-risk areas with precision timing, reducing alert fatigue and false positives that often plague current systems - Reach out to vulnerable patients (COPD, asthma) through text, email, or phone when risk is actually elevated, not just based on static rules - Enable smarter triage decisions at urgent care and PCP levels Prevent unnecessary ED visits (here's where the ROI comes in) One prevented ED visit saves thousands of dollars (maybe more). Most importantly, doing this at the "right" time, not all the time, can save a lot of unnecessary hassles and help us avoid alert fatigue - both for patients and providers. When we combine individual patient data with broader public health context (like this RSV surveillance data), we can make smarter decisions about when to intervene. This shift from reactive to proactive care mirrors what we're trying to achieve with data infrastructure - preventing information delays that lead to missed intervention opportunities while avoiding the burnout that comes from constant, context-free alerts. Although AI/ML gets all the spotlights these days, I often find that the most impactful innovations aren't in complex algorithms, but in building robust data highways that enable timely, informed decisions. Better data infrastructure makes AI more powerful by providing fresher, more actionable training data. After all, value-based care isn't just about savings - it's about right care, right place, right time, in the right hands. By the way, the chart below shows the RSV ED percentage in Georgia broken down by counties. As can be seen, it's the peak season. Be careful out there! [1] https://lnkd.in/eS6ESVv7 #HealthcareInnovation #ValueBasedCare #PopulationHealth #Healthcare #DataAnalytics

Real-time Electronic Health Record (#EHR) data present significant potential for research, but understanding when this data is suitable for analysis is important. Jessica Liu and colleagues developed an automated benchmarking pipeline designed to assess the stability and completeness of real-time EHR snapshots. This system effectively detects clinical actions, demographic updates, and the stabilization of discharge information. The findings indicate that automated benchmarking can determine when real-time EHR data are ready for analysis, which is essential for ensuring trustworthy secondary use of this information. Additionally, this work sheds light on the major challenges associated with using continuously evolving clinical data and introduces an automated framework that health systems can implement to enhance the quality of research, surveillance, and clinical trial preparedness.

Real-time healthcare is no longer a buzzword — Apollo Hospitals just proved what’s possible! We keep on hearing that health systems in India still run on fragmented data, delayed insights, and manual workflows. But Apollo Hospitals – India’s largest private healthcare network – quietly did something game-changing. They rebuilt their entire analytics foundation on Microsoft Fabric, and the results are honestly staggering: 1. Data latency dropped from 4 hours to 2 seconds 2. Manual tracking reduced by 25% 3. Order conversions increased by 60% 4. 230 TB of siloed data compressed into a unified 30 TB system 5. Real-time operational cockpit across 70+ hospitals What does this actually mean? It means clinicians see a live picture of patient flow, bed availability, diagnostics, and discharge readiness. It means executives no longer rely on “gut feel reviews” — decisions are now based on real-time, network-wide performance. As Madhu Sasidhar (CEO) put it: “Our systems must be as responsive and reliable as the people who use them.” And Apollo’s transformation shows exactly what that looks like: Real-time coordination when a patient moves from consultation → diagnostics → billing The part I found most impressive? They designed the system with clinicians, not around them — a masterclass in empathetic digital transformation. And now Apollo is preparing for the next phase: Clinical Copilots, AI agents, automated workflows, and intelligence stitched directly into care pathways. This is the future of hospital operations — not dashboards, but living, breathing operational intelligence!!!! Found this intriguing? Follow the curated network of 5k+ healthtech professionals who engage on topics like these in our community: https://lnkd.in/gqbpj-Nu The Healthtech Collective Dr. Mrudula Bhalke

The FDA just made data streaming the future of clinical trials. On April 28, the agency announced two proof-of-concept studies — AstraZeneca's TRAVERSE in mantle cell lymphoma (MD Anderson + UPenn) and Amgen's STREAM-SCLC — that will pipe safety and efficacy signals to regulators in real time via Paradigm Health's cloud. A broader pilot RFI is open until May 29. This is a structural shift. For thirty years, sponsors batched data into quarterly DSMB reviews and locked the database at the end of a study. Now the FDA is asking for a continuous feed. Streaming is the easy part. Making it actionable is not. A continuous signal is only useful if something is watching it intelligently — separating noise from drift, recognizing emerging safety patterns before a human eye can, and drafting the regulatory narrative that goes with the data. That's where the missing layer is. Pipelines move bytes. Dashboards display numbers. Neither tells you what just happened, what to do about it, or how to write it up for the FDA. At Keiji AI, this is exactly what we've been building TrialMind for. Our agents: → Monitor continuous signals across safety, efficacy, and operational data → Surface and contextualize anomalies in real time — not at the next review meeting → Draft the regulatory deliverables (narratives, tables, protocol amendments) directly from the live data If FDA is mandating data streaming, sponsors will need an intelligence layer on top. Without one, real-time trials just become real-time alarm fatigue. Curious how teams are thinking about this. If you're building toward continuous oversight — or planning a submission to the FDA RFI before May 29 — happy to compare notes. 🔗 https://lnkd.in/efG7nNwi #ClinicalTrials #FDA #AIinHealthcare #RegulatoryScience #RealWorldEvidence

Early in my career, I noticed something that changed how I viewed medicine forever. Too many patients with chronic conditions were coming to the hospital too late. The signs were there hidden in the data but no one was acting soon enough to change the outcome. That realization became the seed for Abiacare, a platform built to harness real-time analytics for proactive, preventive care. Not to collect more information but to give time back. Time for doctors to anticipate rather than react. Time for patients to recover before a crisis begins. That’s when I learned what true healing means it happens when precision meets purpose, when data empowers empathy, and when care moves from reactive to proactive. Here’s what that belief looks like in action: 𒀸 Early prevention – Spot risks before symptoms appear. 𒀸 Actionable insight – Turn data into better, faster decisions. 𒀸 Connected care – Where innovation meets empathy. 𒀸 Improved outcomes – Fewer hospitalizations, faster recovery. 𒀸 Purpose-driven systems – Putting patients first, always. Because the future of healthcare isn’t just about reacting faster, it’s about acting sooner with clarity, compassion, and conviction. How do you see proactive care reshaping the patient journey in the next decade? #ProactiveCare #ValueBasedCare #DigitalHealth #HealthcareInnovation #PredictiveAnalytics #Cardiology #HealthTech #PatientCare #KishlayAnand