Resources
Explore how healthcare organizations, researchers, and life sciences partners from our global community use TriNetX to turn real-world patient data into evidence that drives progress.
With Robert F. Kennedy Jr. now at the helm of the Department of Health and Human Services (HHS), his recent speech to agency staff marked the beginning of what could be a seismic shift in U.S. healthcare policy. Among his key initiatives: launching investigations into whether antidepressants and the childhood vaccine schedule are contributing to chronic diseases.
Combining disparate data sources to inform clinical trial site selection has long been a complex challenge. Inconsistent site naming, differences in the definition of a site, and a lack of potential patient counts, among other obstacles, have made it difficult for sponsors to pinpoint optimal sites.
Real-world data (RWD) and the real-world evidence (RWE) derived from analysis of the data are a cornerstone of clinical advancements in today’s fast-paced research environment
The bispecific antibodies (bsAbs) teclistamab and talquetamab have received United States Food and Drug Administration (FDA) approval for use in relapsed/refractory multiple myeloma (MM) following remarkable single-agent activity in early-phase clinical trials.
The healthcare and clinical research industries are undergoing a seismic shift. Rapid advancements in real-world data (RWD), artificial intelligence (AI), and patient-centric approaches are rewriting the playbook for clinical trials and treatment development. The future of healthcare is here, and organizations that embrace these changes will be at the forefront of innovation.
Clinical trial site identification (ID) and outreach are often inefficient due to manual, paper-based processes, placing a heavy burden on both contract research organizations (CROs) and the clinical study sites with which they work.
Clinical trials are vital to drug development but often face challenges such as slow patient recruitment, high costs, and suboptimal trial designs. Today, artificial intelligence (AI) and real-world data (RWD) are transforming this process by optimizing trial protocols, enhancing recruitment, and accelerating timelines.
The healthcare industry is on the cusp of transformation. Artificial intelligence (AI)-driven insights, real-world evidence (RWE)-backed regulatory decisions, and a new era of patient-centricity are already shaping the next chapter of healthcare.
Clinical trials are the backbone of medical advancements, yet they often face significant hurdles in their final stages.
In the evolving landscape of clinical research, the era of one-size-fits-all clinical trials is firmly behind us. Regulatory bodies and industry leaders alike recognize the necessity of diverse representation in trials to ensure that medical treatments are effective for all populations.
Multiple myeloma (MM) is a rare and complex blood cancer that affects plasma cells in the bone marrow. These malignant cells accumulate and interfere with normal blood cell production, leading to complications such as anemia, kidney dysfunction, bone lesions, and increased susceptibility to infections.
The integration of digital technology is transforming clinical research, improving the efficiency of trial design and execution. A key innovation in this space is the digital waiting room, which leverages real-world data (RWD) and artificial intelligence (AI) to enhance the critical “last mile” of a trial—when sites are activated, and patient recruitment begins.
Upcoming Webinar
From Data to Evidence: A Framework for RWD Study Design & Causal Inference
Thursday, July 16 | 11am ET
Generating credible evidence from real-world data (RWD) requires more than access to data — it demands intentional alignment between the research question, study design, and data source. Yet even experienced teams fall into the trap of starting with the data and working backward, a shortcut that can undermine the trustworthiness of study findings.
Join TriNetX Chief Scientific Officer Jeffrey Brown, PhD, and Miguel Hernán, MD, PhD, ScD, Director of CAUSALab at Harvard, as they walk through a practical framework for designing rigorous RWD studies for causal inference. Attendees will leave with concrete tools for sequencing their research approach, evaluating data fitness, and applying sound decision-making to their own studies.
Join a global community of innovators. Discover how to turn real-world data into groundbreaking clinical insights.
Meet TriNetX at Events
Bridge the gap between raw data and patient care. Meet the team at flagship healthcare conferences across the globe, and learn how to leverage the TriNetX ecosystem to streamline trial timelines, improve site collaboration, and publish research that changes lives.
