Season 2 Episode 8: Richard Young interviews Terttu Haring & Leonie Christianson

This episode of the "State of Digital Clinical Trials" podcast, hosted by Richard Young of Veeva, features Terttu Haring (President of Sites and Patients) and Leonie Christianson (Advisor R&D Practices) from Syneos Health, who discuss the imperative to modernize clinical trials by placing patients and sites back at the center of the clinical strategy. The conversation explores how the industry can leverage new technologies, including AI and advanced data platforms, to overcome fragmentation and ethical challenges posed by the current influx of data, ultimately aiming for faster and more patient-friendly drug development.

Haring and Christianson emphasize that while the industry has better tools and platforms for data aggregation, the focus must shift toward operational efficiency at the site level. Haring, drawing on her experience as a former principal investigator, champions "site centricity," arguing that sites are the operational execution hotspots and must be supported to enroll patients successfully and efficiently extract data. She critiques the industry's historical approach, particularly during the pandemic, of "tipping" technology onto sites without adequate integration, which forced investigators outside their comfort zones and complicated data oversight. The speakers advocate for a model where on-site staff focus only on essential patient care and interaction, while continuous, remote data surveillance and quality checks are handled behind the scenes.

Christianson focuses on the evolution of data management, noting that the industry has perhaps "over-structured" data over time, moving away from the rich, unstructured data of the past. She highlights the transformative potential of modern technology, specifically Generative AI and Natural Language Processing (NLP), to analyze free-text fields, verbal recordings, and other forms of unstructured patient-generated data. This capability allows for the extraction of deeper insights, such as identifying linguistic trends that might signal a patient's risk of dropping out of a trial. Both experts agree that the fragmentation of data management roles—where CRAs and data managers often duplicate checks—must be resolved through integrated data review, advanced risk-based monitoring, and the use of common data platforms that enable real-time collaboration.

The discussion also tackles the terminology surrounding modern trials, with both speakers expressing a strong preference for "distributed" clinical trials over the often-misleading term "decentralized clinical trials" (DCT). They argue that remote data collection has always been a component of trials (e.g., patient diaries). The true opportunity lies in using current mobile and telemedicine technologies to unlock trial participation for any protocol-eligible patient, regardless of their physical proximity to a site. The ultimate goal is to impose best practices, such as ensuring that the industry measures only what truly matters to patients and using integrated systems to accelerate the time from database lock to submission results from months or weeks down to a matter of days.

Key Takeaways:

• Prioritize Site Centricity: Sites must be treated as the operational execution hotspot. Technology implementation should be focused on making the investigator's life easier, ensuring they can focus on patient care and successful enrollment rather than administrative burdens.
• Leverage AI for Unstructured Data Insights: The industry should utilize Generative AI and Natural Language Processing (NLP) to analyze rich, unstructured patient data (e.g., free text, verbal recordings). This capability can reveal powerful, subtle insights, such as predicting patient dropout based on language patterns.
• Integrate Data Review Processes: A major pain point for sites is receiving duplicate queries from different functional groups (e.g., medical, pharmacovigilance, data management). Data management must integrate all data review and querying into a single, cohesive request to the investigator.
• Shift to Distributed Trial Models: The concept of "Decentralized Clinical Trials" (DCT) should be replaced by "distributed" clinical trials. The goal is to use technology (telemedicine, mobile apps) to enable trial participation for any eligible patient, irrespective of geographical location, making remote collection standard practice.
• Focus on Patient Generated Data (PGD): The industry must impose the practice of measuring what truly matters to the patient in their health journey, moving beyond solely clinician-generated or centrally generated data to prioritize PGD.
• Eliminate Data Redundancy: Operational rigor demands eliminating instances where site staff manually transcribe data from one system screen to another, and where CRAs then perform checks on that transcription. Data flow should be automated and continuous.
• Transform Clinical Roles: Technology should drive a clearer differentiation between the roles of CRAs and data managers. CRAs should evolve to focus on supporting site success and patient oversight, while data managers focus on holistic, analytics-based data surveillance.
• Accelerate Data Closure: The ultimate measure of modernization success is speed. New platforms and integrated workflows should aim to reduce the time from database lock to clinical trial submission results from months/weeks to days.
• Avoid Innovation Fear: While risk aversion is a factor, companies should adopt an iterative approach to innovation—short bursts and small implementations—to allow for course correction and build confidence in new methodologies.
• Regulatory Alignment is Key: Regulators should be supportive of applying common medical practices (like telemedicine and remote data collection) to clinical development, provided there is appropriate data quality oversight and risk management.
• Ethical Data Collection: The industry must stop collecting data that is never used in the final submission (estimated at 40-50% of collected data), as this imposes unnecessary cost and burden on sites and patients.

Key Concepts:

• Site Centricity: The principle of designing clinical trial operations and technology implementation around the needs and workflows of the investigators and site staff to maximize efficiency and success.
• Patient Generated Data (PGD): Data collected directly from the patient regarding their health journey, symptoms, and quality of life, often via digital tools, as opposed to data collected by clinicians.
• Distributed Clinical Trials: A preferred term over DCT, emphasizing the use of technology to connect people and data sources across geographical and traditional boundaries, enabling participation wherever the patient lives.
• Integrated Data Review: The process of consolidating data quality checks, medical review, and pharmacovigilance queries into a single, cohesive process to avoid burdening sites with duplicate questions about the same data points.

Tools/Resources Mentioned:

• Veeva Systems: The platform hosting the podcast and a key technology provider in the clinical space.
• EDC (Electronic Data Capture): Mentioned as a technology that, in its early implementation, contributed to the fragmentation of clinical data.
• Generative AI / Natural Language Processing (NLP): Highlighted as crucial technologies for analyzing unstructured patient data (free text, verbal recordings) to gain deeper insights.