TriNetX

Two teams run the same AI… …and get completely different results. Why? Because AI performance is determined by the data beneath it—not the model itself. We break it down here: https://ow.ly/tH2m50Zl7VE #ArtificialIntelligence #AIInsights #DataScience #MachineLearning #AIPerformance #DataDriven #TechExplained #AIModels #DataMatters #InnovationInTech

In clinical research today, having access to millions of records or running algorithms on raw datasets doesn't automatically yield trustworthy results. In this edition of TriNetX Insights, we explore how to turn complex data into credible evidence through two key highlights: Our featured webinar, From Data to Evidence, with speakers Dr. Miguel Hernán from Harvard, and TriNetX CSO Dr. Jeffrey Brown, share a practical target trial framework for aligning research questions, study design, and data sources to ensure true causal inference. In our latest blog post, TriNetX Wins Pinnacle Technology Award, SVP Akiko Shimamura details how grounded, federated data from 300 million patients powers Platinum award–winning AI, delivering real clinical outcomes like a 189% boost in trial recruitment pools and early cancer risk detection. Together, these pieces demonstrate why methodology and data integrity are the non-negotiable foundations for the next era of RWE and clinical AI. #RWD #RWE #AIinHealthcare #MedicalInnovation #Webinar #RealWorldData #RealWorldEvidence #TriNetX

TriNetX LIVE™ Network surpasses 300 million patients, expanding decision-grade evidence across global healthcare. The milestone reflects accelerating international growth and gives pharmaceutical companies, academic researchers, healthcare providers, and health-tech innovators access to trusted, current, clinically rich, and globally representative real-world data. Read the full press release: https://ow.ly/fgm850Zg8yl #TriNetXLIVE #RealWorldData #HealthcareInnovation #ClinicalResearch #MedicalResearch #HealthDataAnalytics #HealthTechInnovation

The qualities that make EHR data most valuable are the same ones that make it hardest to use. And the consequences of using it without the right expertise can quietly undermine your entire study. In the second video of our RWD Insights series, TriNetX Chief Scientific Officer Jeff Brown, PhD draws on 30 years of real-world data experience to break down how to get the most out of EHR data: the opportunities, the pitfalls, and the mindset required. In 8 minutes, you'll learn: → The key strengths of EHR data that claims simply can't replicate → The most common limitations researchers overlook → When to use EHR data, claims data, or a combination of both Watch now: https://ow.ly/KIQe50Zg7Mh #RealWorldData #RWD #ClinicalResearch #HealthcareData #DataScience #MedicalResearch #DataDriven #ResearchMethods

The Pinnacle Awards named TriNetX as a Platinum winner in AI Solutions for Clinical Research & Data Insights at the 2026 Pinnacle Technology Awards. This year’s program drew nominations from across the technology industry. We were recognized alongside winners in categories spanning enterprise AI deployment, generative AI, cybersecurity, and healthtech, a field that reflects how broadly AI has embedded itself into critical industries. Within that competitive landscape, we stood out for a reason that runs deeper than feature development. Read more: https://ow.ly/3CGu50ZnHMv #TriNetX #PinnacleAwards #AISolutions #ClinicalResearch #DataInsights #HealthTech #EnterpriseAI #HealthTechInnovation #AIInHealthcare #ClinicalData #MedicalResearchAI

IBD research is at an inflection point, and traditional methods can't keep pace. Our latest CSO Perspectives Research Impact Report shows how real-world data from the TriNetX LIVE™ platform is helping to answer the most pressing questions in IBD: treatment sequencing, surgical outcomes, safety signals, and more. Five studies. Millions of patients. Decision-ready evidence. Read the full report: https://ow.ly/APP250ZgNJb

Last call to register! Live this Thursday, join Jeffrey Brown, PhD, TriNetX Chief Scientific Officer, and Miguel Hernán, MD, PhD, ScD, Director of CAUSALab at Harvard, for a practical framework on designing rigorous RWD studies for causal inference. You'll leave with: 🔹A framework for sequencing research question, study design, and data source selection 🔹A structured approach for evaluating whether a dataset is fit for purpose 🔹Concrete decision points to apply immediately to your own RWD studies Register here: https://ow.ly/uriX50Zb41S #Webinar #RealWorldData #CausalInference #RWDStudies #Epidemiology #DataScience #ClinicalResearch #HealthData #ResearchMethods #PhDResearch #MedicalResearch

To make better decisions for patients with complex, chronic conditions like Inflammatory Bowel Disease, the life sciences industry needs data that deliver scale, depth, and clinical rigor. Our new CSO Perspectives report highlights how researchers are unlocking deeper insights into IBD. By analyzing real-world outcomes across a vast network, we can move from raw data to actionable evidence faster than ever before. If you’re looking to improve the design of your next protocol or understand evolving treatment patterns, this resource is a great place to start. Read the full report here: https://ow.ly/gFkr50Z7uFQ #ResearchImpact #InflammatoryBowelDisease #IBDResearch #RealWorldData #ClinicalResearch #RealWorldEvidence #MedicalInnovation #HealthcareData #PatientOutcomes #DiseaseProgression

Everyone’s investing in AI. But results are inconsistent… or underwhelming. Why? Because success in AI isn’t about better algorithms, it’s about better data. In this new guide from TriNetX, we break down: 🔹The real reason AI initiatives stall 🔹The 4 pillars of AI-ready data 🔹How leading teams are succeeding Download the guide: https://lnkd.in/esWzhFwC #AIInvestment #DataDrivenAI #AIDataQuality #ArtificialIntelligence #AIGuide #DataScience #TechLeadership #AIInnovation

How much time and money is your team wasting on inaccurate protocol feasibility and site recommendations? When your analytics systems rely entirely on structured EHR fields, you are looking at a systematically incomplete picture of the patient population. For a crucial variable like smoking status, 75% of the data is missing from coded fields. When your data foundation has a blind spot that large, it ripples across: ❌ Protocol eligibility assessments ❌ Site selection efficiency ❌ Trial recruitment timelines We don't have a data collection problem; we have a data extraction problem. The insights exist, but they are trapped in unstructured clinician notes. Learn how TriNetX is recovering this data to build complete patient phenotypes, allowing Clinical Ops teams to design better trials and generate robust real-world evidence (#RWE). Read more: https://ow.ly/r0j950ZgNCU #ClinicalData #AIInHealthcare #DrugDevelopment #DataQuality #ClinicalTrials #HealthcareAI #MedicalData #ClinicalResearch