From Standards To Study Conduct: The Operational Reality Of RBQM
By Life Science Connect Editorial Staff
As regulatory expectations evolve, optimized clinical data management is no longer just a compliance exercise; it has become central to trial efficiency, patient safety, and time-to-market for new therapies. Yet, while digital standards and interoperable systems provide essential infrastructure, they do not automatically translate into better trials.
Operationalizing risk-based quality management (RBQM) requires organizations to rethink how studies are designed, how teams collaborate, and how decisions are made throughout execution. During a recent Clinical Leader Live, panelists offered their perspectives on how to best achieve this operationalization in the current biopharma landscape. Speakers for the event included:
- Julie Smiley, vice president of data sciences, Clinical Data Interchange Standards Consortium (CDISC)
- Joe Fitzgerald, head of clinical data processing and reporting, Regeneron
- Anne Hale, head of study risk management and central monitoring, AbbVie
Together, their insights highlight a growing recognition that the real challenge lies not in defining new frameworks, but in embedding them into day-to-day trial operations. As trial complexity increases and regulatory scrutiny intensifies, the ability to operationalize these principles will increasingly differentiate organizations that can execute consistently from those that struggle to scale.
Prioritizing Critical-to-Quality Elements Early
A foundational principle of effective trial execution is the early identification of critical-to-quality (CTQ) factors — the elements most likely to affect patient safety, regulatory compliance, and the reliability of study results. Defining CTQs at the protocol design stage allows teams to focus on what truly matters, rather than collecting data simply because it has historically been included.
“With regard to the critical-to-quality piece, I think there’s this idea of ‘Oh, if I have more, then we’ll have better data or we’ll have a stronger case,’” Fitzgerald said. “There’s a discipline in being able to say you’ll have two, maybe three [quality tolerance limits] and no more than that.”
This approach reduces operational inefficiencies such as unnecessary protocol amendments, duplicative data entry, and excessive monitoring. It also alleviates site burden by narrowing data collection to endpoints and procedures that directly support study objectives. Importantly, CTQ definition is not a siloed activity. Clinical operations, data management, biostatistics, medical monitoring, and regulatory teams must collaborate to determine acceptable risk and set quality tolerance limits. Early alignment enables more realistic monitoring strategies and prevents downstream overcorrection.
Cross-Functional Collaboration as an RBQM Enabler
Effective RBQM depends on coordinated decision-making across functions. Central monitoring may identify signals, but without clinical and operational context, those signals cannot be translated into meaningful action.
Different therapeutic areas have different “showstoppers,” and collaboration ensures that thresholds are study-specific rather than generic. This prevents unnecessary interventions that disrupt sites while ensuring that true risks to patient safety or data integrity receive immediate attention. Cross-functional engagement also supports regulatory compliance by embedding ICH E6(R3) principles into daily operations rather than treating them as abstract guidelines. Artificial intelligence is increasingly used to analyze large, multi-source datasets, detect anomalies, and support proactive oversight. However, AI’s value depends on governance, documentation, and disciplined interpretation.
A common challenge is defining alert thresholds that balance timely intervention with tolerance for normal variability. Regulators now emphasize that sponsors are expected to adopt documented, risk-based approaches — not exhaustive, blanket monitoring. AI-driven insights are most effective when integrated into a broader RBQM framework that prioritizes CTQs and context-driven decision-making.
AI also plays a critical role in protocol feasibility and historical analytics. By analyzing prior trials, site performance, and eligibility criteria, AI can help teams identify risks before a study begins, reducing amendments and improving recruitment planning.
System Integration and Real-Time Insight
As EDC systems capture a shrinking share of trial data, integration is essential. Without interoperable systems, data aggregation is delayed and risk-based monitoring loses effectiveness. Standards such as HL7 FHIR and USDM enable automated data exchange across platforms, supporting real-time analytics and early signal detection. Integrated ecosystems reduce operational complexity, improve transparency, and allow teams to allocate resources more efficiently. Real-time dashboards enable proactive decision-making without overwhelming site staff, ensuring that oversight remains proportionate and focused.
Clear communication with investigative sites is a critical but often overlooked component of RBQM. When site staff understand why specific data points are prioritized and how monitoring strategies support study objectives, compliance improves and frustration decreases. Transparency builds trust. Rather than perceiving oversight as arbitrary, sites become partners in quality execution.
“I do think it’s a culture shift,” Hale said. “It's the best interest for not only where we're trying to set our sights, but [for] the number of protocol amendments, the site burden to those amendments, all those changes that are coming because of each amendment. That adds additional layers of risk.”
Finally, sustained transformation requires executive buy-in. Leadership must support early investment in CTQ-driven design, interoperable systems, and cross-functional collaboration. While these investments may appear costly up front, the downstream benefits — fewer disruptions, smoother site operations, and faster regulatory pathways — consistently outweigh the cost.