OMOP Gold Data Pipelines for Healthcare AI with David Cecchini

Most discussions about AI in healthcare focus on models, architectures, and benchmarks. But the real bottleneck is something much simpler. Data quality. Clinical data today is: ✔️fragmented across systems ✔️poorly structured ✔️locked in free-text notes and PDFs No matter how advanced an AI model is, if it learns from poorly structured data, the output will never be reliable. This is why the next wave of healthcare innovation will not just come from better algorithms. It will come from better clinical data infrastructure. DocxPanel is one such organisation which is focused on helping transform clinical documentation into structured, research-ready data that can support trustworthy AI systems and global research collaboration. Because in healthcare AI, data quality determines everything. . . . . . . . Dr Sai Ashrit Bandlamudi Dr. Kishore Kumar Dr Hemanth sai Guduru Bhuvanesh kumar www.docxpanel.com #AIinHealthcare #ClinicalData #ResponsibleAI #HealthTech #DocxPanel #DigitalHealth #MedicalAI #ClinicalWorkflows #HealthTechInnovation #ResponsibleAI #MedicalResearch #HealthData #AIethics #GlobalHealth #MedicalResearch #AIinMedicine

📣 Transforming Multimodal Clinical Data into Regulatory-Grade Insights In this webinar, led by John Snow Labs CEO David Talby, you will learn how to transform multimodal clinical data into FDA-aligned, regulatory-grade evidence. We’ll present a scalable secondary-use architecture designed to convert fragmented raw records into enriched, OMOP-coded datasets optimized for Agentic AI. Discover strategies for automating patient registries, accelerating trial matching, and synthesizing longitudinal patient stories while maintaining the rigorous data governance required for regulatory submissions. Finally, evaluate the benchmarks necessary to move AI-driven abstraction from "experimental" to "production-grade" for complex oncological use cases. 📅 Mar 31, 2026 | 1:00 pm - 2:00 pm EDT Register now: https://lnkd.in/gtvW3Y2D #HealthInformatics #DigitalHealth #AI #HealthcareAI

Healthcare doesn't have an information problem. It has a reconciliation problem. The evidence exists. The guidelines exist. The device specifications exist. The regulatory constraints exist. The patient data exists. But none of those things sit inside the same reasoning environment — and none of the AI tools being deployed today were designed to reconcile them against each other before producing output. General-purpose AI platforms provide language capability. They summarize, they draft, they answer. They are fast and often impressive. But healthcare doesn't run on impressiveness. It runs on precision, accountability, and traceability — in environments where the cost of a wrong answer is not a corrected document, but a harmed patient, a compliance failure, or an institutional liability. MedSync AI was built for that environment. Not as a chatbot with a clinical interface. As a structured intelligence architecture that enforces guideline hierarchy, IFU-based device constraints, and regulatory alignment before producing any output. Every recommendation is traceable to a validated source. Every interaction is audit-ready. The reasoning is deterministic, not probabilistic — which means it behaves the same way in a high-acuity decision at 2am as it does in a training session on a Tuesday afternoon. Where general AI generates a response, MedSync governs a decision. That distinction is not a feature. It is the architecture. 🔗 medsync-ai.com #HealthcareAI #MedSyncAI #ClinicalAI #AIGovernance #TrustedAI #HealthSystems #DigitalHealth #StructuredIntelligence #PatientSafety #HealthcareInnovation

Covering AI, Technology & Innovation......Catch up on our latest Hot Topic discussion...... The ACDM: Association for Clinical Data Management would like to thank Hemant Gawande of Cognizant for leading our recent Hot Topic Discussion on "AI Companion for Clinical Data Manager.” This session explored how the role of IT support is changing and what this means for Clinical Data Managers. It also introduced the CDM Intelli Workbench, a companion tool designed to support data managers across study set-up, conduct and close-out. The recording of this session will be available in the membership area of the website. ACDM Members can also log in to view recordings of the previous Hot Topic Discussions. ACDM Board: Robert King, Sverre Bengtsson, Nina Reyes, Eva Alder, Richard Davies, Nicola Götz, Anita Kratchmarov, Ashley Howard, Lisa Moneymaker, Yuvarajan P. #clinicalresearch #clinicaltrials #clinicaldatamanagement #clinicaloperations #ACDM

I recently watched the recording for the ACDM: Association for Clinical Data Management Hot Topic on 'AI Companion for Clinical Data Manager' presented by Hemant Gewande. It was really interesting to see the potential for agentic AI across Clinical Data Management processes from specification creation to validation agents and the predicted related cost and time reductions. While agents could draft, build and test, human-in-loop validation by CDMs will remain vital for GXP compliance. However, my question is where will the CDMs of the future gain their knowledge base to validate the AI results? As a Clinical Data Associate for the last 11 years, data listings, reconciliations, and query management were my daily activities and have given me a strong foundation in data validation processes. What will be left for those entering our profession in future to gain hands on experience of data validation when AI is performing the review activities for us? I would love to hear your thoughts!

The AI for business is becoming increasingly relevant, and the appreciation of business problems will be crucial for true transformation. Join us at ACDM Berlin, where our head of Life Sciences solutions, will be discussing this with industry leaders. Ghananeel Gondhali

From Risk to Revelation......The AI/ML Leap and the Clinical Data Science Landscape...... This ACDM26 panel discussion will explore how AI and machine learning are already shaping clinical data management, from automated query generation to predictive risk monitoring. Our panelists will share practical experiences of how these technologies are being adopted today, while also discussing the emerging potential of agentic AI and how data managers can evolve into strategic partners within increasingly AI-driven data ecosystems. Hear from: > Tanya du Plessis, Bioforum the Data Masters > Ashley Howard, Pfizer > Helen P., PureCDM > Matthew Adams, ICON plc > Ghananeel Gondhali, Hexaware Technologies View the ACDM26 programme, and book your place here: https://lnkd.in/dWjzqJp ACDM26 Conference Programme Committee: James McKenna, Ali Roskell, Stephanie Hau, Hari Priya, Sandy Pinto, Lynette Thomas, Jasvinder Osan, Amanda Bravery ACDM: Association for Clinical Data Management Board: Robert King, Sverre Bengtsson, Nina Reyes, Eva Alder, Richard Davies, Nicola Götz, Anita Kratchmarov, Ashley Howard, Yuvarajan P., Lisa Moneymaker #clinicalresearch #clinicaltrials #clinicaldatamanagment #clinicaloperations #ACDM #ACDM26

Most healthcare data is incomplete. Not because we lack technology. But because we built the system on biased foundations. If AI is trained on incomplete data, it does not improve healthcare. It scales the problem. In a recent discussion with Roswitha Verwer and Dr. Muskaan Bhan, one insight stood out: Almost none of today’s AI medical systems are trained on female-specific data. This is not a technical gap. It is a structural failure. In this article, I explore why the future of healthcare depends on continuous data, inclusive datasets, and the rise of FemTech as a scientific discipline rather than a niche. If we want better outcomes, we need better data. Read more here: https://lnkd.in/ghFtiZ9Q

Discover the 5 benefits of Artificial Intelligence in EDC and data management in this carousel! AI-powered EDC helps teams reduce errors, cut manual queries, prioritize high-risk sites, and accelerate database closure. Smarter monitoring. Better data. Faster decisions.

Discover the 5 benefits of Artificial Intelligence in EDC and data management in this carousel! AI-powered EDC helps teams reduce errors, cut manual queries, prioritize high-risk sites, and accelerate database closure. Smarter monitoring. Better data. Faster decisions.