Beyond the Buzzwords: What SCOPE 2026 Revealed About Clinical Trial Planning and Operations

Across a multitude of insightful sessions from the 17th annual SCOPE meeting in early February, a unifying theme emerged: The research and development industry is rapidly moving from fragmented, document‑driven processes toward connected, data‑centric ecosystems. Regardless of clinical trial phase, experts underscored how standardization, interoperability, and patient‑focused methodologies are reshaping trial design, execution, and oversight.

Together, these conversations revealed not only the maturity of technologies that were considered experimental not long ago, but also how scientific and operational excellence must converge to support more efficient, predictable, and patient‑aligned trials. Below, we dive deeper into some of the critical signals from SCOPE 2026 that will shape the future of clinical research.

Journey to the digital protocol

The industry is evolving from document‑centric clinical protocols to structured, computable protocol data. This shift reflects a broader recognition that traditional protocol documents—which are often lengthy, unstructured, and manually interpreted—can bottleneck operational efficiency, interoperability, and real‑time insight generation. This is especially true as clinical trial design grows more complex.

The SCOPE 2026 panel titled “Journey to the Digital Protocol” brought together leaders from a global clinical research organization and several large pharmaceutical firms to share insights on how the industry is shifting toward digitizing protocols as a standards‑driven, machine‑readable representation of study elements that can be directly consumed by downstream systems to enable a true “write once, reuse everywhere” paradigm.

The discussion underscored the industry’s acceleration toward standardization and interoperability, showing how structured digital protocol elements eliminate redundant data entry across key systems, such as interactive response technologies, electronic data capture, clinical trial management systems, and consent and safety platforms, while speeding study start‑up and reducing protocol‑driven errors. Panelists emphasized that automation and artificial intelligence (AI)–driven study intelligence depend on high‑quality, structured protocol data, since consistent inputs are essential for generating reliable predictions around study design, patient burden, site selection, and operational risk. By transforming the protocol from a static document into a dynamic digital asset, organizations can orchestrate workflows, automatically trigger downstream processes, and enable more adaptive, data‑driven decision‑making across the trial life cycle.

Understanding that organizations sit at different maturity stages, with some operating with document‑only protocols while others are piloting structured data frameworks or deploying digital protocols in production, the consensus among experts was that digital protocols form the backbone of the next era of automation, efficiency, and intelligence in clinical research.

A fresh look at data sharing for study planning and site selection

Siloed data holds and other long-standing barriers to stakeholder collaboration have limited the industry’s collective ability to make informed, data-driven decisions about investigators, sites, and study feasibility that would ultimately help improve patient care. But the evolution of the Data Query System (DQS) and cross-sponsor data-sharing partnerships is helping research and development stakeholders move past several historical challenges: limited collaboration due to confidentiality concerns, lack of master data management, inconsistent terminology, and an absence of interoperable standards. These factors have historically resulted in siloed datasets, heavy manual reconciliation, and inefficiencies in site selection and feasibility planning. Without shared identifiers or a common data model, sponsors often duplicated qualification activities, burdening investigators and slowing study timelines.

At SCOPE, in an insightful and needed discussion of the value of industry-level data sharing for enhanced study planning and site selection, R&D experts traced the industry’s 20-year journey toward more coordinated data sharing. The process began with the Investigator Databank in 2012, then moved through expansion phases, including support for the Investigator Registry, and culminated in today’s DQS ecosystem with 23 participating sponsors.

Panelists said a central and critical component of this ecosystem is the “Golden Number,” a unique identifier enabling accurate matching of investigators, site staff, and facilities to studies across disparate datasets.

Beyond sharing data, DQS aligns multiple data sources, including sponsors’ clinical trial management system data, public registries, and clinical trial databases, to provide a single, holistic, and duplication-free view of global investigator activity and site capabilities. In practice, this can mean:

• Sponsors have more realistic enrollment projections and a better understanding of countries to prioritize in outreach, ultimately reducing the reliance on rescue sites for under-enrolling studies and nonperforming sites.
• Sites can benefit from reduced administrative burden and more targeted alignment with studies suited to their expertise and resources.

Panelists said the key takeaway from the global network of investigators is the development of a shared resource. Supported by strong governance, standardized identifiers, and transparent collaboration, data sharing maximizes industry capacity, improves predictability, and supports the collective mission of developing therapies efficiently for patients worldwide.

De-risking eCOA commoditization: Understanding the past to transform the future of eCOA

R&D experts at SCOPE reflected on the profound shifts within the electronic clinical outcome assessment (eCOA) landscape. From early vendor-driven scientific rigor to a decade of market commoditization, and now, a renewed focus on scientific integrity, there is a critical question on the minds of sponsors and contract research organization partners. As eCOA has become more widely adopted, has the industry unintentionally drifted away from the scientific foundations required to ensure high-quality patient experience data, and, if so, is it possible to course correct?

During the early era of eCOA development, from 1998 to 2012, deep domain expertise, close sponsor-vendor relationships, and scientific oversight defined the space, with intensive implementation heavily guided by behavioral scientists and COA experts. However, as demand grew, rapid consolidation and scaling pressures led to a focus on speed, cost, and technical features rather than methodological rigor. In many cases, commoditization resulted in a “rinse and repeat” operational cycle that often excluded scientific input, leading to nonscientific designs, costly mid-study redesigns, data reliability issues, and fragmented delivery models with multiple third-party handoffs.

At the same time, regulatory expectations for high-quality, fit-for-purpose patient experience data increased dramatically. With 68% of FDA reviews mentioning patient experience data and 30% of approved labels incorporating it, the scientific importance of eCOA is higher than ever.

Together, this helped trigger a noticeable return to the integration of rigorous, science-based COA strategies with advanced tech innovation across the full COA life cycle.

Experts noted the importance of integrating scientific rigor with innovative technology through end-to-end COA delivery services. This entails a one-stop shop to help define the right COA strategy and measures up front. It also ensures continuity and accuracy when mapping the scientific framework to a patient-friendly, fit-for-purpose eCOA solution, without the delays and inconsistencies caused by external handoffs. Strong operational oversight—supported by implementation science—allows teams to rapidly detect compliance risks and data anomalies, ensuring datasets remain reliable and regulatory ready.

When this scientific foundation is paired with modern technology, including flexible deployment models, deep system integrations, expansive COA libraries, automated scoring, and emerging AI-driven quality checks, the result can be a faster, more predictable, and higher-fidelity data pipeline. Layered with patient-centric features, such as “bring your own device,” wearables integration, training modes, and simplified user experiences, this end-to-end model ensures fit-for-purpose patient experience data while reducing the burden on sites, clinical statisticians, study teams, and, most importantly, patients.

Redefining industry foundations

Collectively, the presentations point to an industry that is dramatically shifting from isolated systems and manual interpretation to integrated, comprehensive platforms powered by structured data, intelligent automation, and renewed scientific stewardship.

Whether through digital protocols that drive downstream processes, shared investigator data that strengthen site selection, eCOA strategies that restore rigor to patient experience measurement, or the spectrum of other takeaways coming out of SCOPE ’26, the direction is clear: Clinical research is entering an era of smarter design and more reliable execution. As organizations embrace these advances, success will depend on aligning technology with scientific principles and cross‑industry collaboration to deliver higher‑quality trials with greater efficiency.

About the authors

Melissa Mooney, Director, Solution Engineering, Patient Suite, IQVIA

Mooney has more than 2 decades of experience in the development of eCOA solutions for use in clinical trials. Her extensive experience in eCOA solution design has helped clients and eCOA vendors develop robust, usable eCOA software solutions that meet eCOA protocol requirements. Mooney is also an expert in gathering eCOA requirements, leading eCOA user acceptance testing, and overseeing eCOA data management strategies.

Nicholas Whitney,​ Senior Director, Site Suite, IQVIA

Whitney has more than 15 years of experience in the life sciences industry. In his current role, he is responsible for overseeing technologies that improve study planning, feasibility, study start-up, and conduct, and guiding customers to bring value from technology into trial operations.

In prior roles, Whitney has worked with a broad array of real-world evidence/real-world data stakeholders, including academia, payers, government, disease foundations, and patient advocacy organizations, supporting the design and delivery of patient registries, observational studies, and clinical trials.

Cara Woodruff, Director of Product Management, IRT, IQVIA

Woodruff has nearly 3 decades of industry experience in biopharmaceutical research and development. In her current role, Woodruff is responsible for defining product vision and roadmaps, executing product strategy, driving product-release cycles using agile development methods and partnering with cross-functional teams to deliver high-quality, high-value solutions for life sciences companies.