Jens Dommel’s Post

The power of linking multiple RWD sources for mapping the patient journey Join this webinar where industry experts discuss… + benefits & limitations of leveraging various RWD sources such as EHRs, images, claims, and genomic data + value of linking RWD sources for different use cases + case studies utilizing data linked from multiple RWD sources #RWD #PatientInsights #IntegratedEvidencePlanning

Antimicrobial resistance isn’t just a future threat — it’s unfolding every day inside hospitals. Inappropriate or delayed antibiotic use fuels resistant infections, increases length of stay, and complicates care. AI is emerging as a powerful ally in antimicrobial stewardship. By harnessing real-time data, it can support clinicians in making timely, targeted prescribing decisions and optimize hospital-wide antimicrobial practices. Here are a few practical ways AI is helping reduce AMR: Early detection of resistance patterns → AI rapidly analyzes lab and clinical data to spot emerging resistance trends before they escalate. Optimized antibiotic selection → Decision support tools suggest appropriate agents and dosing based on local antibiograms, patient factors, and evolving resistance data. Stewardship workflow support → AI helps identify cases for review, flag prolonged or broad-spectrum use, and streamline team interventions. How is your institution currently approaching antimicrobial stewardship, and where could AI make the biggest impact? Let’s share real-world experiences to advance this critical area of patient safety. #DoctorsAIGlobalSummit2025 #ICAIM #DoctorsAI #AIinHealthcare #AntimicrobialStewardship #AMR

📊 Monday Read: The Hidden Pandemic Cost While fighting COVID-19, antimicrobial stewardship took a major hit: - 81% drop in antibiotic reviews - 70% fewer AMS audits - 67% reduction in IV-to-oral switches Why AI matters in AMS: AI analyses prescribing data in real-time to identify antibiotic misuse patterns, predict resistance trends, and provide evidence-based recommendations at the point of care—even during crises like pandemics. These intelligent tools flag inappropriate prescriptions, suggest optimal alternatives, and continuously learn from outcomes to improve. AI augments clinical judgment with data-driven insights that humans cannot process at scale. Bottom line: We need resilient, AI-enhanced systems to combat antimicrobial resistance while handling future healthcare disruptions. Research by Elshenawy et al., published in JAC-Antimicrobial Resistance, July 2025: https://lnkd.in/eHygzwjs #AntimicrobialResistance #HealthcareInnovation #DigitalHealth #MondayRead University of Hertfordshire @JAC-AMR British Society for Antimicrobial Chemotherapy (BSAC)

The challenge of accelerating personalised healthcare for children with rare and complex diseases demands a pioneering, data-driven approach. Our innovative 5-year collaboration with Great Ormond Street Hospital for Children NHS Foundation Trust (GOSH) is a leading example of how the NHS and industry can successfully unite to solve complex health challenges; a key call to action in the Life Sciences Plan (2025). We were delighted to host Professor Neil Sebire, GOSH's Chief Research Information Officer and co-lead of the partnership, at our UK Innovation Day. He powerfully articulated how our joint efforts are harnessing AI and data to accelerate personalised healthcare for children with rare and complex diseases, and highlighted key successes from the partnership: 🧰 AI tools: the Genomic Information Finder (GIF) tool, which securely unlocks vital genomic insights from PDF reports in hours rather than months, and PICTURE, which helps authorised healthcare professionals analyse patient information to inform clinical decision-making. The code for both tools has been open-sourced on GitHub, making the technology available for adoption across the wider NHS. 📋 Genomic standards: co-developing a Genomics FHIR data schema, which is now being shared with NHS England to shape national genomic data standards. 💻 Tackling the digital shift: how our work directly supports key goals in the NHS 10-year plan, particularly the fundamental challenge of moving from analogue to digital. 🌎 Real-world progress: by co-developing digital, AI tools, we are overcoming infrastructure barriers and data fragmentation that often results in ineffective care. We are proud of our collaboration with GOSH, which is truly delivering the "triple win": tangible benefits for patients, crucial efficiencies for the healthcare system and valuable collective learnings for the entire sector. This work exemplifies Roche's leadership in transforming patient care through safe, ethical and scalable data science. Great Ormond Street Hospital DRIVE unit #DigitalHealth #AIinHealthcare #NHSPartnerships

AI in Healthcare: Roche’s Kidney Klinrisk Algorithm Earns CE Certification for Early CKD Detection In a major step toward predictive and preventive medicine, Roche Diagnostics, in partnership with KlinRisk Inc., has achieved CE certification for its AI-driven Kidney Klinrisk Algorithm — a digital tool designed to estimate the risk of kidney function decline. Part of the Navify® Algorithm Suite, the Kidney Klinrisk Algorithm uses machine learning to analyze patient data, helping clinicians identify and monitor individuals at early or high-risk stages of Chronic Kidney Disease (CKD). By combining the Kidney Klinrisk Algorithm with the Kidney Failure Risk Equation (KFRE), Roche provides a powerful clinical toolkit that supports timely interventions and improved patient outcomes. This development showcases how AI can transform healthcare — from reactive disease management to proactive prevention. #AIinHealthcare #DigitalHealth #HealthTech #RocheDiagnostics #KidneyHealth #ChronicKidneyDisease #MachineLearning #HealthcareInnovation #PredictiveAnalytics #MedTech #ClinicalAI #Navify #KlinRisk https://lnkd.in/gBff9bTr

Co-designed by Chiesi Global Rare Diseases and EVERSANA, this interactive session brings together clinicians, patients, policymakers, subject matter experts, and industry innovators to explore how technological, clinical, and organizational innovations — particularly AI — are transforming rare disease care and helping shape concrete actions toward an enabling health innovation ecosystem across Europe. Proud to contribute to this important discussion at #WODC — at the intersection of innovation, policy, and health impact - together with: @MaurizioScarpa @KimAngel @AlbertaSpreafico @MonicaOtto @EnricoPiccinini and MEP Vytenis Andriukaitis (@European Parliament) In particular, I am excited for the opportunity to discuss how together with NCATS at the NIH, my team and I are moving beyond rule-based rare disease detection or ML models limited to one condition at a time. Instead we are developing diagnostic AI agents that combine real-world data, reference knowledge, leveraging platforms that create graph databases, use LLMs, BioBERT, and neuronetworks to accelerate the diagnosis of rare diseases earlier and more accurately. This is a new path toward scalable,deployable framework into decision support tool intended to support physicians at the point of care Join us by registering here: https://lnkd.in/e5CgRWHA  #RareDiseases #LSDs #DigitalHealth #AI #HealthInnovation #WODC25; # EVERSANA

Researchers at Auburn University Montgomery are leveraging AI to predict urinary tract infections before they manifest, enabling tailored treatments and faster recoveries. By analyzing patient histories and microbial data, this approach promises to reduce trial-and-error antibiotic use and curb resistance. As data-driven medicine transforms healthcare, predictive AI is key to personalized, cost-effective care. How can we extend these predictive models to combat other infectious diseases and revolutionize patient outcomes? #ArtificialIntelligence #MachineLearning #HealthTech #PredictiveAnalytics #DigitalTransformation #Innovation #SaaS #HealthcareAI #FutureOfMedicine

Just finished reading the 𝗞𝗣𝗠𝗚 and 𝗙𝗜𝗖𝗖𝗜 𝗿𝗲𝗽𝗼𝗿𝘁, "𝗔𝗜 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: 𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗲 𝗰𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻," and here are the 3 most value-adding takeaways: 1️⃣ A major hurdle to scaling AI is that many healthcare organizations are currently 𝘀𝘁𝘂𝗰𝗸 in the 𝗽𝗶𝗹𝗼𝘁 𝗽𝗵𝗮𝘀𝗲 due to challenges like fragmented implementations, unclear returns on investment, and significant cultural resistance. 2️⃣ Public health systems are leveraging AI for advanced, specific applications like 𝗰𝗹𝗶𝗺𝗮𝘁𝗲-𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝗱𝗶𝘀𝗲𝗮𝘀𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 and 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗲𝗽𝗶𝗱𝗲𝗺𝗶𝗼𝗹𝗼𝗴𝘆 for better preparedness and efficiency. 3️⃣ In radiology, AI is already widely adopted, with over 𝟭,𝟬𝟬𝟬 𝗨𝗦𝗙𝗗𝗔-𝗮𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 that perform high-value tasks such as 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗿𝗮𝗱𝗶𝗮𝘁𝗶𝗼𝗻 𝗱𝗼𝘀𝗲 in CT/PET-CT and 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝗰𝗮𝗻 𝘁𝗶𝗺𝗲𝘀. The path to reimagined care requires turning proof-of-concepts into strategic, widespread transformation. Thanks for the report: Harsh Mahajan Varun Khanna Prof. Anupam Sibal Lalit Mistry Ashutosh Raghuvanshi Alisha Moopen Upasana Arora Gautam Khanna Dr Raajiv Singhal Sanjeev Kumar Singh Shuchin Bajaj Asit Kumar Vidyarthi Dr. Shreeporna Bhattacharya Nipen Dutta Rimjhim Mishra Meet Thaker Sidhant Jain

🏥 AI + Cloud = Smarter Healthcare ☁️ Frankfurt University Hospital is transforming patient care with SAS Viya on Amazon Web Services (AWS) — using real-time analytics to predict infection trends, optimize antibiotic use, and manage hospital resources more efficiently. With AWS providing the secure, scalable backbone, clinicians now access insights faster, improve patient outcomes, and reduce operational strain. A great example of how cloud + AI can make healthcare smarter, safer, and more efficient. 🔗 Read the story: https://lnkd.in/eN9X5bp7 #AWS #HealthTech #Healthcare #CloudComputing #SAS #DigitalTransformation