Electronic Medical Records Systems

Will AI Make Traditional EMR Integration Obsolete? As we step into 2025, one of the most exciting tech trends transforming industries—especially healthcare—is AI-powered EMR integration. For decades, interoperability has been one of the biggest challenges in healthcare. Every hospital and provider uses different EMR systems, creating data silos that slow down patient care and drive up costs. Traditional integrations relied on APIs, complex engineering, and costly maintenance—but AI is changing the game. 🔹 AI can standardize and translate diverse EMR data in real-time. 🔹 It reduces implementation time and eliminates manual reconciliation. 🔹 Scalability improves as AI adapts to new systems without re-engineering. 🔹 Cost savings are substantial, reducing reliance on middleware solutions. By automating data harmonization, AI is making healthcare more connected, efficient, and patient-centric. This shift doesn’t just optimize hospital operations—it enhances patient outcomes by ensuring that data flows seamlessly across providers, enabling better care decisions. Will this mean the end of the traditional EMR integration industry? Possibly. But what’s certain is that AI-driven interoperability is set to reshape the future of healthcare. #LEAP25 #AI #HealthcareInnovation #DigitalHealth #Interoperability #EMR #HealthTech

One of the challenges that many healthcare organizations face is how to make the huge volume of data they generate work for them. Uses depending on the organization include research, clinical operations, clinical trials, learning health systems, business research, innovation, development, strategic planning and decision making, policy planning etc. Some have figured it out but many still struggle with being ‘data rich but insights/wisdom poor’ due to poor data strategy to aggregate data across sources, data structures and types, multiple practices or institutions, fragmented technology systems, multiple EHRs, connecting non-clinical data etc. This publication on NIH’s All of Us Data and Research Center which summarizes the principles and lessons learned from creating an ecosystem for biomedical research. The guiding principles, the multilevel access for a balance of transparency and privacy, and use of published standards including HL7 FHIR, OHDSI OMOP CDM Standards for health data and the Global Alliance for Genomics and Health standards for Genomic data, are part of industry best practices. https://lnkd.in/eKdEVhcq Links to learn more about each standards are included (in addition you may like this amazing introductory video to HL7 FHIR by Russell Leftwich MD FAMIA at this link https://lnkd.in/epQrRYdV). Kudos to the NIH All of Us teams, participants, and contributors for the ongoing work and taking the time to share their experience with the community. #datamanagement #biomedicalresearch #interoperability #healthcareinformatics #dataanalytics #realworldevidence #datascience #innovation #researchanddevelopment #learninghealthsystems #aiandml

Oracle takes on Epic with voice-driven, AI-enabled electronic health records: 💿Oracle Health has unveiled a new electronic health record for U.S. outpatient clinics, built from scratch in the cloud rather than patched onto old Cerner systems 💿 Doctors can now use simple voice commands to pull up lab results, medications or patient summaries, cutting out multiple clicks and screen-hopping 💿 The system’s AI has been trained on real clinical data, so it can surface the right insights at the right time instead of just dumping information 💿 It also takes on admin tasks like documentation and coding, with clear guardrails showing where AI is used and what the limits are 💿 Clinics can stick with Oracle’s AI tools, build their own, or plug in third-party models, a more flexible approach than many competitors 💿 Designed with input from frontline clinicians, the system is meant to adapt to each user over time, aiming to feel more like a consumer app than clunky medical software 💿 Oracle is rolling this out just as Epic, its biggest rival, prepares its own AI features #digitalhealth #ai

📊 Most medical data lives in unstructured clinical notes, and too often, it not used. Every day, physicians write progress notes, imaging reports, and pathology summaries filled with signals: treatment responses, adverse events, even early signs of success or failure. But buried in free text, this information is unusable for traditional analysis. Clinical trials remain the gold standard, but they’re expensive, slow, and often unfeasible. So how do we unlock the evidence hidden in messy medical records? That’s what TRIALSCOPE set out to do. 🔹 By combining biomedical language models, probabilistic approaches, inferences, TRIALSCOPE automatically structured EMR data from over 1 million cancer patients. 🔹 It reproduced results of published lung cancer trials, generalized to pancreatic cancer, and simulated studies. 🔹 Compared with manual curation, it achieved >20× faster processing and 10× lower cost. -> Clinical text isn’t noise. With the right tools, it’s raw data waiting to become real world evidence.

We’re starting to see the first cracks in Epic’s dominance. At this year’s UGM, Epic announced Microsoft will power its AI scribe. On the surface, that looks like a win. In reality, it’s a bearish signal: Epic can’t attract or retain the AI talent to build this in-house. As AI becomes the core of clinical workflows, health systems aren’t going to settle for bolted-on solutions — they’ll demand world-class AI built directly into their EMR. AI scribes like Abridge, Ambience, and Nuance are useful — but they’re still just add-ons. Think of them like a better AC unit in a car. No one buys a new car just because the AC got better. Epic can keep patching on upgrades, but it doesn’t change the fundamentals. A truly AI-native EMR is different. It’s not an accessory; it’s a reimagining. Tesla didn’t disrupt Ford and Toyota by building a better AC — it rebuilt the car from first principles, with a new engine, supply chain, and consumer promise. The same is true here. An EMR built from scratch on AI-native design principles — structured data capture, ambient automation, intelligent workflows — has the potential to unseat the incumbents. Obviously, this won't happen tomorrow. Epic’s moat is real: distribution, lock-in, regulatory muscle. But these cracks are widening, and in 5–10 years, they’ll matter. AI scribes won’t kill Epic. But they expose the weakness that could. The opportunity is building the Tesla of EMRs — not the aftermarket AC unit. (H/t Anjini K. for the Tesla analogy!)

Epic Systems and AI at point of care. Epic is poised to drive the adoption of AI in the industry with their significant technology presence in healthcare settings. During their recent Users Group Meeting, Epic introduced a range of AI solutions aimed at supporting clinicians, empowering patients, enhancing operations, and fostering research. Key innovations include: - Clinical Documentation (Art): An ambient AI assistant that captures real-time clinical encounters, reducing documentation workload, and simplifying order entry. - Patient Engagement (Emmie): An intelligent assistant aiding patients in preparing for visits, understanding lab results, managing preventive care, and navigating their healthcare journey. - Revenue Cycle & Operations (Penny + AI Suite): Tools for automating coding, supporting denial management, and optimizing workflows like discharge planning, surgical risk assessment, and patient flow. - Cosmos AI: Utilizing predictive models trained on Epic’s extensive de-identified Cosmos dataset, covering over 300 million patients and 16 billion encounters. These models facilitate research, predictive analytics, and scalable decision support, offering early insights into outcomes such as diagnoses and readmissions. The impact of these advancements includes: - Boosted clinician efficiency through automation and reduced administrative tasks. - Enhanced patient experience with personalized, easy-to-understand insights and proactive guidance. - Financial stability via AI-driven revenue cycle enhancements. - Accelerated research and innovation fueled by a vast real-world healthcare dataset. https://lnkd.in/eV4D3uvP

🧬🩺 The Evolution of EMR & EHR: From Paper to AI-Powered Digital Health Records⚙️ The transformation of healthcare records has been nothing short of revolutionary! From the first Electronic Medical Record (EMR) system at Massachusetts General Hospital in the 1960s to today’s AI-driven EHRs, we have witnessed a massive shift in how patient data is stored, accessed, and utilized. 🚀 Key Milestones: ✅ 1960s: COSTAR—One of the first EMR systems ✅ 1980s: VA’s VistA system—A foundational EHR model ✅ 1996: HIPAA—Laying the groundwork for data security ✅ 2009: HITECH Act—Incentivizing EHR adoption ✅ 2014: FHIR—Enabling modern API-driven healthcare interoperability ✅ 2020s & Beyond: AI, automation, and predictive analytics transforming EHRs 🔥 What’s Next? AI-powered Clinical Decision Support (CDS), voice-based documentation, FHIR-based interoperability, and blockchain for data security are shaping the next decade of digital health. 📌 Are you a Business Analyst? Learn EMR/EHR workflows, interoperability (FHIR, HL7), and compliance (HIPAA). 📌 Are you a Developer? Focus on API integration, cloud platforms, and AI-driven data processing. 📌 Are you a Data/AI Specialist? Dive into NLP, predictive analytics, and AI-driven automation in EHR systems. #DigitalHealth #EHR #EMR #HealthcareIT #FHIR #Interoperability #HealthTech #AIinHealthcare #BusinessAnalysis #HealthcareInnovation

Interoperability is not a Platform, It’s an Evolving Capability: Step-by-Step Roadmap for Data Interoperability
 (Fresh, practical, and aligned with modern tech trends)   1. Diagnose the Data Disconnect Why it matters: Understand where integration fails and what it costs the business. Actions: -Use data lineage tools (e.g., Collibra, Alation) to auto-map data silos, legacy connectors, and flow bottlenecks. -Run a maturity diagnostic focused on governance, quality, and system interoperability. -Pinpoint root causes like format mismatches (XML vs. JSON), brittle ETL, or API fragmentation.   Outcome: Heatmap of friction points tied to real-world impact (e.g., delayed closings, NPS drop).   2. Anchor Interoperability to Business Objectives Why it matters: No point fixing pipes unless it fuels outcomes that matter.   Actions: -Align with business imperatives: e.g., real-time 360, ESG reporting, IoT-led efficiency. -Use OKRs for precision targeting. o Objective: Cut reconciliation time by 70%. o Key Result: Adopt FHIR for patient data or AGL for vehicle telemetry.   3. Architect for Flexibility and Scale Why it matters: Interoperability is not a platform, it’s an evolving capability.   Options: -Data Mesh: Empower domains with ownership and APIs (e.g., supply chain owning SKU data products). o  Tools: Starburst Galaxy, Confluent. -Data Fabric: Auto-discover and govern with ML-driven metadata (e.g., Informatica CLAIRE). -Infrastructure: o  Cloud-native + serverless (AWS Lambda, Azure Synapse). o  Edge-first for latency-sensitive IoT workloads.   4. Standardize with Open APIs Why it matters: Without shared protocols, integration becomes brittle and expensive.   Actions: -Enforce open standards: o  Healthcare: FHIR + SMART. o  Manufacturing: MTConnect. o  Global: JSON-LD. -Build API-first ecosystems: o  Use GraphQL for dynamic querying, AsyncAPI for event-driven models. -Use smart gateways (Apigee).   5. Leverage AI for Intelligent Interoperability Why it matters: Manual mapping can’t keep pace, automation is non-negotiable.   Actions: -Use Gen AI to auto-map schemas (e.g., CSV → FHIR-compliant JSON). -Deploy ML-driven data quality tools (Monte Carlo, Great Expectations). -Accelerate integration using low-code platforms like Power Automate.   6. Embed Federated Data Governance Why it matters: Centralized governance slows agility. Federated = control with speed.   Actions: -Assign Data Product Owners for accountability. -Automate policy enforcement (Policy-as-Code via HashiCorp Sentinel). -Apply zero-trust sharing.   7. Pilot Fast, Prove Value, Scale Hard Why it matters: Show early ROI to unlock buy-in and budget.   Actions: -Pick high-ROI pilots. -Track KPIs: Latency <100ms, error rate <1%, adoption >80%. -Scale using Agile sprints and replicate via Infrastructure-as-Code.     Continue in first comment.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: ISACA

This paper evaluates the use of generative AI and large language models in healthcare, focusing on their application to electronic medical records (EMRs). It presents a framework for evaluating these models' effectiveness and discusses the pathway toward their implementation in clinical settings. 1️⃣ Introduction of ChatGPT by OpenAI highlights the potential of generative AI in healthcare, capable of tasks like passing medical exams and interpreting EMR data. 2️⃣ Wornow et al.'s review (https://lnkd.in/deUE86hj) of 84 foundation models for EMRs identifies limitations like lack of generalizability and data privacy issues, proposing a new evaluation framework. 3️⃣ The framework focuses on predictive performance, data labeling, model deployment, emergent clinical applications, multimodality, and novel human-AI interfaces. 4️⃣ Recent integrations of generative AI in health records by companies like Microsoft and Oracle Cerner illustrate the practical application and evaluation of such models. 5️⃣ The paper emphasizes the need for leadership, incentives, and regulation to ensure the effective and ethical implementation of generative AI in healthcare. This paper underscores the importance of generative AI in revolutionizing healthcare by enhancing predictive performance, simplifying model development, and reducing deployment costs. However, it also calls for a comprehensive approach involving leadership, regulation, and continuous evaluation to overcome challenges related to generalizability, data privacy, and model hallucination. ✍🏻 Marium Raza , Kaushik Venkatesh, Joseph Kvedar. Generative AI and large language models in health care: pathways to implementation. npj Digit. Med. 7, 62 (2024). DOI: 10.1038/s41746-023-00988-4

Part of Inter-American Development Bank's digital health case study series, the team has recently made the Implementation Process for the Unified Electronic Medical Record (UEMR) in Bogotá, 🇨🇴 case available in English. 🛣️ Quite an insightful journey: kudos to the team for making it available for the non-Spanish speaking audience - Fernando A. Portilla V. Luis Tejerina! Bogotá, with its 8 million residents, is a major healthcare hub in Colombia. The UEMR project and its key learnings have significantly influenced country's interoperability strategy, reflected in the Agenda de Transformación Digital e Interoperabilidad 2022-2031. This effort positions Colombia as a one of leading examples of healthcare digital transformation in Latin America. 💡Interestingly, the project, which kicked off in 2016 with a strong reliance on IHE XDS architecture and HL7 CDA, has gradually evolved to embrace FHIR. This shift made the UEMR to operate in a more flexible by also more complex, blended exchange mode. Any lessoned learned you could share Fernando? 🗒️ This is something to consider for us in 🇪🇺 as we navigate the gradual implementation of the EHDS's MyHealth@EU infrastructure, particularly as we plan for a longer transition period from CDA to FHIR. Ander Elustondo-Jauregui Alexander Berler Prof. Georgi Chaltikyan, MD, PhD Miguel Coelho Benedikt Aichinger Konstantin Hyppönen