IOMED

Healthcare systems generate enormous volumes of clinical data every day. Yet a large part of this information remains locked within unstructured clinical documentation. Artificial intelligence is opening new possibilities to unlock this data and make it usable for research, evidence generation, and innovation. Here are 5 ways AI can unlock value from hospital data: • Identifying patient cohorts that match complex clinical criteria • Extracting key clinical information such as diagnoses, biomarkers, treatments, and outcomes • Structuring narrative clinical documentation into analyzable data • Harmonizing heterogeneous hospital datasets into interoperable formats • Enabling responsible secondary use of health data for research When clinical data becomes structured, interoperable, and research-ready, it can accelerate clinical trials, feasibility studies, and real-world evidence generation, while supporting hospitals in participating in the future European Health Data Space ecosystem. At IOMED, we work every day to help unlock the value of hospital data for research and innovation. #AIinHealthcare #RealWorldData #ClinicalResearch #HealthData #EHDS

Turning complexity into trust: why validation is key for real-world data. The use of real-world data in healthcare is growing fast, powering research, accelerating innovation, and informing better care decisions. But behind every reliable dataset, there’s one essential ingredient: trust. And trust starts with quality. At IOMED, we ensure this quality through a two-layered approach: - Verification, where reviewers check samples of extracted data for accuracy. - Validation, where we assess entire datasets to ensure they are consistent, complete, and clinically meaningful. Our latest article dives into this validation layer, and why it’s so critical for transforming clinical data from messy to meaningful. Because in the end, data validation isn’t just a technical step. It’s what turns information into actionable, trustworthy insight, the kind that researchers and clinicians can rely on to improve patient outcomes. 👉 Read the full article: https://lnkd.in/ds9EiM2N  #RealWorldData

Detecting the unseen: how AI helps uncover rare kidney diseases Chronic and hereditary kidney diseases (HKD) often remain underdiagnosed, but early detection can change everything.  Together with Fundació Puigvert, IOMED applied AI and Natural Language Processing (NLP) to real clinical data to identify potential Alport Syndrome cases hidden in medical records. By combining standardization of hospital data using OMOP CDM, HPO-based NLP extraction to capture phenotypic features, and AI clustering and similarity models to detect hidden patterns, the study uncovered 50 potential undiagnosed patients. This collaboration shows how structured real-world data and AI can accelerate early detection of rare diseases, bringing us closer to precision medicine. 👉 Read more: https://lnkd.in/dXkpRV8x

At IOMED, we’re redefining clinical research by leveraging artificial intelligence (#AI) to unlock the full potential of real-world data (RWD). Our advanced Data Space Platform, powered by AI, revolutionizes the way large volumes of medical data are structured and standardized across diverse sources and formats. By utilizing Natural Language Processing (NLP), we transform free-text clinical notes into actionable data, enabling faster and more accurate research outcomes. This groundbreaking approach doesn’t just enhance data analysis; it accelerates clinical trials, reduces costs, and accelerates the development of innovative treatments. With deep, high-quality RWD available at every stage, we are helping to generate real-world evidence (RWE) that drives informed clinical and strategic decision-making. At IOMED, we are committed to advancing healthcare by ensuring more efficient clinical trials and better outcomes for patients worldwide. 🌍💡 https://lnkd.in/d6xTeNSM

Building Trust in Healthcare: Why Real-World Data Validation Matters The adoption of real-world data in healthcare is expanding rapidly, driving research, supporting innovation, and guiding better clinical decisions. But behind every high-quality dataset lies a fundamental element: trust. And trust begins with ensuring data quality. We maintain this quality through a two-step approach: - Verification: sampling extracted data to check for accuracy. - Validation: evaluating entire datasets to confirm they are complete, consistent, and clinically meaningful. Our article explores this validation process and explains why it’s essential for transforming raw clinical data into reliable insights. Ultimately, data validation is more than a technical procedure—it’s the foundation that turns complex information into actionable, trustworthy knowledge that researchers and clinicians can rely on to improve patient care. 👉 Read the full article: https://lnkd.in/ehGSpY6E #RealWorldData

Proud to share this Applied Clinical Trials  research article, to which Gabriel Maeztu , CTO & co-founder at IOMED, contributed as a co-author, exploring one of the most pressing challenges in clinical research today: how to activate unstructured clinical data to truly scale eSource-enabled trials. The article is based on in-depth expert interviews across hospitals, pharma, and technology providers, and reflects a shared conclusion: eSource will not scale without addressing unstructured data, validation, and trust. It brings together perspectives from experts at i~HD, Cambridge University Hospitals, AstraZeneca, Johnson & Johnson, ZS Associates, University Hospital of Essen, IgniteData, Evidentli, and others, highlighting the need for collaborative, federated validation models and human-in-the-loop AI to make unstructured data truly regulatory-grade. 🙏 Grateful to all co-authors and contributors for the exchange and collaboration Mats Sundgren  Sarah Burge  Lars Fransson  Adriano Garcez MSc, MBA  Joeri Holtzem  Thomas Metcalfe  @stevenftolle   Felix Nensa  Joe Lengfellner  #eSource #ClinicalTrials #UnstructuredData #HealthData #AIinHealthcare #RealWorldData #Interoperability #FHIR #OMOP #HealthTech

Real-World Clinical Data for Clinical Research – Data Space Platform At IOMED, we’ve developed a Data Space Platform designed to improve interoperability and accelerate clinical research. 🔷 The challenge: Up to 80% of clinical data is unstructured (e.g., free text, pathology reports, discharge summaries), making it difficult to reuse. In addition, regulatory, ethical, and administrative hurdles often slow down research projects. 🔷 The solution: Our Data Space Platform (DSP) enables: ➡️ NLP and automated terminology mapping to convert unstructured data with >85% accuracy. ➡️ Transformation of free-text into structured data in OMOP-CDM, supporting multi-center studies. ➡️ Streamlined approval and mediation workflows between hospitals and researchers. ➡️ Full GDPR compliance and adherence to hospital policies. 🔷 Impact: ➡️ Faster approvals and study initiation. ➡️ Improved data quality and completeness. ➡️ Greater hospital participation in collaborative, multi-center research. A significant step toward a secure, federated, and efficient healthcare data ecosystem. https://lnkd.in/gYJaqU32

Turning rare disease expertise into computable clinical knowledge. In rare diseases such as Alport syndrome, identifying patients who may remain undiagnosed requires more than access to data. It requires deep clinical expertise, a structured understanding of disease phenotypes, and the ability to detect those patterns across routine healthcare records. In this video, Dr. Elizabeth Viera, Nephrologist and Principal Investigator at Fundació Puigvert, and Gabriel Maeztu M.D., Co-founder and CTO at IOMED, explain how the collaboration between both teams helped translate rare disease expertise into computable phenotypes. By accurately detecting relevant HPO terms, the algorithm enabled the identification of known Alport patients and supported the search for clinically similar cases — helping uncover new potential undiagnosed patients. This project shows how AI-powered technology, when guided by clinical knowledge and validated with healthcare professionals, can help transform real-world clinical data into actionable insights for earlier rare disease detection. At IOMED, we are proud to collaborate with Fundació Puigvert on projects that demonstrate the real-world value of responsible AI in healthcare. #RareDiseases #AlportSyndrome #Nephrology #ArtificialIntelligence #ClinicalData #RealWorldData #NaturalLanguageProcessing #HealthcareInnovation #IOMED

Viability in clinical research depends on having a deep, accurate understanding of the patient population, not just high-level estimates. It requires comprehensive insight into real clinical profiles to ensure that studies are realistic, relevant, and grounded in real-world data. IOMED enables this through a secure federated network of strategic Data Holder partnerships. Powered by proprietary NLP technology, the platform unlocks the full patient record, extracting structured data and rich context from clinical notes, reports, procedures, diagnoses, treatments, and lab results. Download the whitepaper and see how 👇 https://lnkd.in/eMG2Ka3E

Real-World Clinical Data for Clinical Research - Data Space Platform At IOMED we have developed a Data Space Platform that enhances interoperability and accelerates clinical research. 🔷 The challenge Up to 80% of clinical data is unstructured (free text, pathology reports, discharge summaries), making reuse difficult. On top of that, regulatory, ethical, and administrative processes significantly delay research projects. 🔷 The solution Our Data Space Platform (DSP): ➡️Uses NLP and automated terminology mapping to structure unstructured data with >85% accuracy. ➡️Transforms free-text into structured data in OMOP-CDM, enabling multi-center studies. ➡️Streamlines the approval and mediation workflow between hospitals and researchers. ➡️Ensures GDPR compliance and alignment with hospital policies. 🔷 Impact ➡️Shorter approval and study initiation times. ➡️Higher data quality and completeness. ➡️Increased hospital participation in multi-center projects. A step forward towards a secure, federated, and efficient health data ecosystem. https://lnkd.in/dEtWZSrJ