ICH E6(R3) has made Quality by Design (QbD) a regulatory imperative, but to truly reap the benefits, QbD must move beyond theory into a living, breathing framework that shapes every trial decision. Is your team ready?By translating QbD principles into concrete actions, you can reduce rework, strengthen patient safety, and deliver reliable, inspection-ready data—without slowing timelines.
- Map QbD principles to each phase of trial design, from concept through close-out
- Identify and prioritize CTQs early to prevent downstream quality issues
- Integrate QbD with RBQM frameworks for a cohesive, risk-driven approach for operational efficiency and regulatory readiness
- Leverage technology and analytics to make QbD a continuous, adaptive process rather than a one-time exercise
In today’s regulatory environment, RBQM isn’t optional, but successful adoption is far from guaranteed. Too many organizations invest in tools and frameworks only to see them stall under cultural resistance, leadership disengagement, and outdated “business as usual” mindsets. The cost of failed adoption? Missed risk signals, wasted resources, and diminished trial quality.
Join us to experience a complete change management roadmap—starting with building urgency, mobilizing champions, and securing leadership buy-in, then sustaining momentum through clear metrics, visible results, and organization-wide engagement. Through this you can master practical strategies to transform RBQM from a compliance project into a lasting cultural shift.
- Recognize the most common adoption roadblocks and how to overcome them
- Reframe RBQM concepts into language teams embrace
- Build a network of cross-functional champions to lead peer-to-peer adoption
- Maintain executive sponsorship by demonstrating ROI and strategic value
- Keep RBQM visible, relevant, and embedded in daily operations long after rollout
Risk isn’t an abstract exercise or a compliance checkbox, it’s part of every decision we make, every day. Yet in many organizations, risk management is either misunderstood, overcomplicated, or avoided entirely. Drawing from the science of human learning theory, you will see how to strip away the jargon and help teams of any size engage meaningfully with RBQM, transform assessments into real action, and build a culture where everyone sees risk management as an enabler—not an obstacle.
- Reframe risk management as an everyday decision-making tool
- Simplify RBQM language and draft communication plans to boost cross-functional engagement
- Translate risk identification into meaningful mitigation plans with measurable outcomes
- Foster buy-in from teams that have historically resisted risk-based approaches
- Apply education techniques that make complex topics stick
A 2024 survey by Tufts CSDD, CluePoints, and PwC found that, on average, companies have implemented RBQM in only 57% of their clinical trials. RBQM adoption often stalls when ownership is limited to a single function or small group of specialists. Effective, sustainable implementation requires broad engagement from clinical operations, data management, biostatistics, quality, and other key functions. Real-world lessons from CRO and pharma settings can give you clearer understanding of the organizational, cultural, and operational shifts needed to embed RBQM principles across teams.
- Grasp the risks of siloed RBQM ownership and the benefits of cross-functional engagement
- Implement strategies for bringing clinical operations, data management, and other leaders into the RBQM process early
- Apply change-management tools to build trust and reduce resistance
- Establish shared accountability for quality, efficiency, and cost outcomes
- Integrate RBQM into existing workflows without undermining established best practices
In today’s evolving RBQM landscape, fractured relationships between sponsors, CROs, and sites can quietly erode both quality outcomes and diversity goals. Add in shifting inclusion mandates and decentralized trial models, and the stakes for alignment have never been higher.
Your team needs to equip its clinical leaders with the strategies to transform tension into trust—aligning all parties on shared risk ownership, transparent communication, and inclusive Quality by Design (QbD) principles.
- Define and align shared risk ownership across sponsors, CROs, and sites to eliminate role confusion and prevent blame-shifting
- Integrate diversity and inclusion goals into QbD frameworks, even amid regulatory rollbacks
- Translate inclusion principles into measurable risk and quality metrics that resonate with all stakeholders
- Adapt recruitment and consent strategies to meet evolving regulatory requirements without compromising data quality
- Foster cross-functional trust that accelerates decision-making and ensures trials remain both representative and compliant
Groundbreaking research from the Tufts Center for the Study of Drug Development is shedding light on the true business case for Risk-Based Quality Management. Leveraging industry benchmarks and impact measures from actual RBQM implementation experience, Tufts CSDD modeled the Expected Net Present Value (eNPV) of RBQM investment in oncology drug development programs. The findings reveal substantial financial returns, providing long-awaited evidence to support adoption, scale, and continued investment.
- Understand the eNPV modeling and direct cost ROI methods used to quantify RBQM’s financial impact
- Identify the key drivers of value when RBQM components are implemented
- Apply evidence-based insights to strengthen internal business cases for RBQM adoption
- Anticipate future directions in measuring the ROI of RBQM deployments
The most advanced RBQM processes mean little without the right people to execute them. Yet organizations across the industry are struggling to find professionals who understand both clinical trial operations and data-driven quality management—a rare “unicorn” skillset.With E6(R3) adoption accelerating the need for skilled RBQM practitioners, the pressure is on to build and train teams that can handle regulatory demands, embrace new tools, and collaborate seamlessly across functions. You will benefit from practical strategies to identify, recruit, and retain the right talent—while also upskilling existing staff.
- Define the essential skills and roles needed for effective RBQM implementation
- Explore models for distributing RBQM responsibilities across existing teams versus creating dedicated roles
- Develop strategies for recruiting and retaining rare cross-functional talent
- Learn approaches to upskilling site staff, CRAs, and data teams to support RBQM goals
- Align staffing plans with evolving regulatory, operational, and technological demands
AI is no longer a futuristic concept, it’s already embedded in tools that claim to predict and prevent trial risks before they escalate. But the reality is more complex: without the right human oversight, data integrity safeguards, and thoughtful implementation, AI can create as many problems as it solves.Can your team cut through the hype to examine where AI genuinely adds value, where its boundaries should be drawn, and how to launch high-impact pilots without compromising compliance or patient safety?
- Evaluate AI’s real capabilities in detecting and predicting trial risks
- Define the level and type of human oversight required for safe, effective AI use
- Identify high-impact, low-risk AI pilot projects that deliver measurable results
- Understand regulatory expectations and ethical considerations in AI-driven RBQM
- Integrate AI insights into existing processes without disrupting team workflows
With the ICH E6(R3) adoption now in effect and global enforcement on the horizon, RBQM leaders face a pivotal moment: moving from theory to tested, operational practice. A 2025 DIA survey showed that 67% of trial sponsors feel only “partially ready” for full enforcement, so how are these changes playing out inside organizations and what early lessons can we take forward?
Learn from real-world post-implementation stories with forward-looking strategies, highlighting what worked, what failed, and what’s still up for debate. Identify the most impactful process changes organizations have made in response to E6(R3).
- Pinpoint common implementation missteps and how to avoid them
- Develop a flexible RBQM plan that adapts to evolving enforcement interpretations
- Translate compliance mandates into operational wins for sites and sponsors
Is your team ready to turn hidden process data into actionable intelligence? Audit trail analytics, spotlighted in ICH E6(R3), is rapidly becoming a must-have capability for Risk-Based Quality Management. By unlocking earlier issue detection, sharper site performance insights, and proportional risk-based responses, it empowers teams to elevate both compliance and operational efficiency.
Explore a practical three-domain framework—Clinical Relevance, Data-Driven Analytics, and Operational Integration—that shows exactly how to put audit trail analytics to work. Through real-world examples and hands-on exercises, you’ll practice setting up an audit trail analysis for a common risk scenario, running root cause investigations, and embedding the results into everyday RBQM workflows for lasting improvement.
- Discover why audit trail analytics is essential for safeguarding data integrity and site performance
- Apply a proven three-domain framework to drive compliance and operational efficiency
- Place audit trail analysis into action for real-world risk scenarios
- Use root cause analysis techniques to uncover the “why” behind findings
- Translate insights into sustainable improvements that strengthen RBQM programs
Regulators don’t just evaluate processes—they test your organization’s ability to respond under pressure. Whether in pharmacovigilance, clinical trials, or manufacturing, inspection readiness is a cross-functional challenge that exposes every weakness in risk ownership, communication, and documentation.Using collaborative exercises and live AI synthesis, the group will build a practical, inspection-ready risk strategy that can be adapted to their own organizations – a tangible framework you helped create.
- Identify the most common breakdowns in inspection preparedness across PV, GCP, and manufacturing functions
- Practice scenario-based problem-solving to strengthen risk response strategies
- Define clear roles and responsibilities for inspection day success
- Leverage AI tools to capture, refine, and document strategic outputs in real time
- Build a reusable compliance readiness framework tailored to cross-functional teams
Critical to Quality (CTQ) elements define what matters most in your trial, Key Risk Indicators (KRIs) monitor the ongoing health of those elements, and Quality Tolerance Limits (QTLs) set the boundaries for acceptable variation. Can your team align these three components to form a powerful, proactive RBQM framework that drives quality, safeguards patient safety, and ensures inspection readiness?
- Identify and prioritize CTQs that align with protocol endpoints, patient safety, and regulatory expectations
- Select KRIs that meaningfully measure risk to CTQs and provide actionable insights
- Set QTLs that act as early-warning signals, catching issues before they derail timelines or data integrity
- Foster collaboration between clinical, data management, and quality teams to manage CTQs, KRIs, and QTLs in unison
- Avoid “overengineering” by focusing on metrics and limits that are both meaningful and achievable
- Translate CTQ/KRI/QTL outputs into targeted monitoring strategies that keep RBQM agile and inspection-ready
Human factors engineering is a cornerstone of medical device safety, ensuring usability and minimizing risks associated with user interaction. Clinical trials, however, often underestimate the impact of human factors on data integrity and patient safety. Principles from medical device usability engineering can be applied to RBQM frameworks to identify and mitigate risks related to human error in trial processes.
- Highlight the role of human factors in risk management for medical devices and its relevance to clinical trials.
- Learn practical methods for incorporating usability principles into RBQM risk assessments.
- Identify strategies to reduce human error and improve trial quality through proactive design and monitoring.
RBQM KPIs only matter if they shape leadership decisions and drive RBQM adoption. This is particularly true for small to mid-sized companies where resources and expertise can be a challenge. Do your KPIs go beyond operational checkboxes to tell a clear, compelling story about value, quality, and return on investment? Take a deep dive into selecting, designing, and presenting KPIs that drive executive-level decision-making and promote RBQM adoption. Learn how to choose metrics that matter to both scientific and financial stakeholders and present results in a way that supports strategic priorities.
- Select metrics that balance clinical relevance and cost efficiency
- Present KPIs in a way that drives strategic decision-making
- Link RBQM outcomes to broader business objectives to secure ongoing support
Despite contributing less than 2% to overall data quality, Source Data Verification (SDV) remains one of the most expensive and resource-intensive elements of clinical trial monitoring. As the industry pushes toward risk-based methodologies, is SDV a legacy practice we can finally retire—or does it still serve a vital role in ensuring trial integrity?
Proponents will argue its continued necessity in high-risk areas, while challengers will make the case for smarter, targeted approaches that free up resources without compromising safety. Attendees will leave with a clearer understanding of whether and how SDV adds value or slows progress.
- Evaluate the real ROI of SDV in the era of risk-based monitoring
- Identify trial types, endpoints, and risk profiles where SDV remains critical
- Compare cost, efficiency, and quality outcomes between 100% SDV and targeted SDR
- Explore change-management tactics to move teams beyond entrenched SDV habits
Fraud in clinical trials isn’t always obvious—but it can seriously compromise data integrity, patient safety, and regulatory compliance. From “professional patients” enrolling in multiple studies to duplicate enrollments across sites, fraudulent activity can distort outcomes and erode trust. Advances in statistical monitoring and data science now make it possible to detect these risks earlier and more accurately. Does your team have the tools to safeguard data integrity, protect patient safety, and stay inspection-ready?
- Recognize common indicators of fraud, including duplicate enrollments and “professional patients”
- Explore statistical monitoring and data analytics techniques for anomaly detection
- Understand the capabilities and limitations of tools such as CluePoints and broader patient identity–matching systems
- Integrate fraud detection seamlessly into RBQM processes and centralized monitoring strategies
- Respond effectively when suspicious patterns are detected, balancing regulatory requirements with site relationships