By Heather Grey, SVP & GM, Camille McWhirter, VP of Sales and Taylor Elkow, Associate Director of Sales at Omega Healthcare.
Most patients receive care in community health centers, yet the vast majority of clinical trials still take place in large academic medical centers, creating major gaps in access, equity, and scientific validity. Expanding trials into community settings is essential to ensure therapies are tested in diverse populations.
In 2026 and beyond, AI will play a critical role in making trials more patient-centric and inclusive. Today, AI is no longer a vague, intimidating concept or an unknown looming in the background.
Instead, healthcare organizations are asking practical questions about how AI can empower them to improve trials. The ability to clean data more efficiently, support more clinical trials, and elevate the quality of research through AI implementation is very real today, something that simply wasn’t achievable in the same way even two years ago.
In addition to AI broadening access to trials, here are some other trends we expect to see within the clinical trials space in 2026.
Data volume explodes: As clinical trials increasingly extend beyond the four walls of academic medical centers, the volume of data generated is growing at an unprecedented pace. Traditional trials typically capture data at discrete study visits, but modern trials now collect information continuously, across days, weeks, or months, through electronic health records, remote monitoring tools, and patient-reported outcomes. This explosion of data has enormous potential to deepen clinical insights, but it also places new demands on trial infrastructure, analytics, and governance.
Clean data is essential: Real-world data (RWD) has become a cornerstone of clinical research, offering the ability to validate findings, inform trial design, and support more personalized treatment decisions. However, clean, usable data remains the exception rather than the rule. Researchers, principal investigators, and frontline clinicians alike often struggle with fragmented, inconsistent, and unstructured data that limits its practical value. As trials grow more data-intensive, the ability to transform raw information into reliable, FDA-grade datasets will be essential to maintaining scientific rigor, meeting regulatory expectations, and ensuring results can be audited and reproduced.
Growth in wearables: Wearable technologies and remote therapeutic monitoring are rapidly moving from niche tools to mainstream components of clinical research. Devices embedded in everyday life, whether dedicated medical-grade wearables or consumer technologies integrated with EHRs, are expanding how and where data can be collected. This trend is particularly powerful for community health centers and rural sites, which can now participate in research without requiring frequent in-person visits. As adoption grows, organizations will need clear strategies for validating, integrating, and governing this influx of device-generated data.
More decentralized trials: Decentralized and hybrid trial models are reshaping the patient experience and broadening access to research. Increasingly, patients can participate through home visits, telehealth appointments, and virtual check-ins rather than traveling to centralized research facilities. These approaches reduce burden on participants while generating highly data-rich insights across diverse populations and disease states. As this model matures, success will depend on strong operational support, seamless data flows, and consistent data quality across distributed settings.
How Omega Healthcare can help
As clinical trials become more decentralized, data-rich, and operationally complex, success increasingly depends on having the right combination of people, process, and technology behind the scenes. Omega Healthcare supports clinical trials, real-world data initiatives, and registries by addressing the operational challenges that most often delay or derail research. Trials frequently fail due to inconsistent data, staffing shortages, missed timelines, and lack of audit-ready processes. Omega Healthcare was built to solve those problems, helping research teams keep trials on track and bring therapies to market faster.
CurateIQ: Omega Healthcare’s CurateIQ platform bridges the gap between having large volumes of data and having data that is actually usable for research, regulatory submission, and AI initiatives. CurateIQ transforms raw, unstructured clinical data into research-ready, FDA-grade datasets held to 95% or greater accuracy. This work is performed by a large team of physicians, nurses, oncology specialists, and domain experts, with AI layered in to enhance, not replace, clinical expertise. The result is faster drug development, fewer downstream corrections, and data that is repeatable, auditable, and trustworthy. Importantly, Omega Healthcare does not retain rights to or copies of any data it touches; its role is purely to curate and structure data to meet each client’s specific needs, without data hoarding or resale.
AbstractIQ: In parallel, Omega Healthcare’s AbstractIQ and registry services help health systems meet growing compliance, reporting, and accreditation demands. Omega Healthcare’s registry experts and certified Oncology Data Specialists (ODS) manage organization-specific, centralized, and state-mandated registries, equipping clients with ODS-certified registrars to handle everything from case finding and abstraction to quality audits, outcomes reporting, and surveys. This support reduces backlogs, improves data quality, and ensures regulatory timelines are met across cancer, trauma, stroke, chest pain, bone marrow, and other registries. By integrating registry management with broader research and data curation services, Omega Healthcare enables health systems to reduce operational burden while strengthening the foundation for clinical trials and real-world research.
Together, CurateIQ and AbstractIQ help organizations turn data chaos into clarity — supporting cleaner data, stronger trials, and more equitable access to research across both academic and community-based settings.
Conclusion
In 2026, clinical trials will be defined by scale, complexity, and opportunity. Exploding data volumes, the rise of wearables, and the shift toward decentralized models are expanding what is possible, but only if data is clean, structured, and trustworthy.
Real-world data and AI are no longer abstract concepts; they are practical tools that can improve access, accelerate timelines, and strengthen scientific validity when applied thoughtfully. Organizations that invest now in data quality, operational infrastructure, and patient-centric approaches will be best positioned to run more inclusive, efficient, and impactful trials in the years ahead.
