Investigative Sites

From payment delays and feasibility misalignment to technology burden and AI adoption, clinical research sites are navigating a convergence of pressures that increasingly determine who sponsors work with and how well trials perform.

Limited evidence surrounds direct-to-patient clinical trial site models, despite growing literature showing that decentralized approaches can improve patient access, enrollment performance, and operational efficiency while reducing participant burden.

In this episode of the Applied Clinical Trials Podcast, Jonathan Andrus, co-CEO, CRIO, and Samir Jain, vice president of product management, healthcare data interoperability and EHR solutions, Medidata, discuss how their new partnership is enabling seamless data flow between eSource and enterprise platforms to reduce site burden and improve data quality across global clinical trials.

Payment experience—including timeliness, transparency, and consistency—is emerging as a key operational factor influencing site financial stability, working relationships with sponsors and CROs, and overall clinical trial execution.

Clinical trial delays often originate in early site selection decisions, where misalignment between protocol demands and site capabilities undermines startup, enrollment, and data quality despite later efforts to correct course.

Behavioral science reveals how recruitment failures, site disengagement, and underrepresentation in clinical trials are rooted in early design decisions, and what sponsors can do to address them before they become costly problems.

The execution translation gap—the failure to convert identified problems into coordinated, timely action—costs millions per trial through delayed amendments, persistent deviations, and slow site activation, yet remains addressable through aligned accountability and proactive execution management.

A collaborative study by the Tufts Center for the Study of Drug Development and CRIO identifies protocol interpretation and source document preparation as an understudied yet significant bottleneck in study start-up timelines that may hold key opportunities for efficiency gains.

In this Q&A, Krishna Cheriath, VP and head of clinical research digital data and AI at Thermo Fisher Scientific, examines how AI is reshaping clinical operations—from case intake and trial design to site burden reduction and the emerging reality of agentic AI in the workforce.

Risk-based monitoring requires integrated data systems, validated analytics, and strong governance to work effectively across global trials, but sponsors face significant technical and operational challenges that demand strategic solutions and organizational alignment.

In this Q&A, Cheryl Kole, vice president of solution strategy and commercialization at Almac Clinical Technologies, examines what it takes to build and sustain a clinical trial technology infrastructure that can keep pace with increasingly complex study designs.

In part 2 of this three-part series, behavioral science and service design reveal how poor experiences with confusing information, unreliable technology, and inefficient processes drive site disengagement and patient dropout, and how measuring these experiences early enables practical, evidence-based solutions.

Vaccine developers can reduce participant burden and extend follow-up timelines by strategically combining traditional site visits with real-world data collection, but the choice depends on follow-up duration, data requirements, and the patient population being studied.

As clinical trials grow more complex, the technology infrastructure supporting them is under renewed scrutiny. Across data validation, AI adoption, and site-based systems, 2026 is shaping up as a year of implementation rather than experimentation.

Investigator-initiated trials operate under the same regulatory and operational requirements as industry-sponsored studies but rely on fragmented, consumer-grade tools, creating a persistent technology gap that can impact data integrity, efficiency, and compliance.

At SCOPE Summit 2026, site leaders shared how AI is transforming feasibility, patient identification, and enrollment strategies, enabling research sites to boost performance, strengthen sponsor relationships, and deliver more precise, patient-centered clinical trials.

Jonathan Andrus, co-CEO of CRIO, highlights the need for earlier cross-functional collaboration and greater site involvement to ensure data quality, workflow alignment, and operational success.

Jonathan Andrus, co-CEO of CRIO, outlines how disconnected systems and inconsistent data collection across sites create risk, while centralized eSource approaches present a major opportunity.

Insights from SCOPE 2026 highlight the industry’s shift toward connected, data-centric clinical trial ecosystems, where digital protocols, shared data, and renewed scientific rigor are driving more efficient, interoperable, and patient-focused research.

Jonathan Andrus, co-CEO of CRIO, explains how protocol-driven eSource templates and standardized data capture are improving consistency, oversight, and efficiency across clinical trial sites.

Jonathan Andrus, co-CEO of CRIO, discusses how increased reliance on site-based technologies and eSource is strengthening data quality, compliance, and trust at the point of patient encounter.

Angela Zubel, chief development officer, Debiopharm, explains how AI-enabled site selection, patient allocation, and real-time data monitoring can reduce costs, shorten timelines, and limit inefficiencies caused by non-performing sites.

Holly Leslie, vice president of services at Ledger Run, discusses how persistent payment friction, increasing administrative burden from AI-generated queries, and lack of sponsor accountability are pushing sites to become more selective—favoring sponsors that pay transparently, reduce operational strain, and treat site experience with the same rigor as patient recruitment.