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Track Summary • SCOPE Summit 2025

Quality & Monitoring

Mastering Risk-Based Strategies and Embracing Innovation for Tomorrow’s Trials

Contents

Overview

Across sessions, risk‑based quality management (RBQM) emerged as a practical, upstream discipline that starts at protocol design, relies on standard data and lean metrics, and channels finite expertise toward a few signals that measurably impact participant safety and scientific credibility. Speakers described teams naming Critical‑to‑Quality (CTQ) factors early, assigning accountable leaders, and keeping thresholds simple so mitigations flow into everyday plans rather than piling up in dashboards or duplicating on‑site work that fails to change outcomes. Scale came from choosing regulatory‑ready datasets over raw system feeds, running unsupervised models on quarterly cycles across portfolios, and co‑creating summarized outputs that give monitors, statisticians, and medical reviewers the big picture without adding new tools or heavyweight infrastructure. Audit‑trail analytics moved to the center as inspectors now request explicit evidence of data integrity and risk control, which drives investment in machine‑readable logs and shared dashboards that surface issues earlier - View . Inclusion advanced in parallel, with Social Determinants of Health (SDOH) models shaping protocol choices, site selection, and outreach plans, and early results showing prioritized centers enrolled twice as many minority participants as peers - View .

What Converged Across Sessions

Leaders agreed RBQM demands years to embed - View . Executive sponsorship accelerates that adoption across functions - View . Teams defined CTQs during Quality‑by‑Design planning - View . Teams also set Quality Tolerance Limits (QTLs) early - View . AbbVie showed SDTM standards unlock enterprise‑scale analytics - View . An internal RStudio server supported the approach - View . Their pipeline delivered portfolio‑wide runs within hours - View . As a result, audit‑trail analytics became central under inspector scrutiny - View . Fraud risks demanded CTQ‑agnostic integrity checks - View . SDOH tools guided inclusive site selection and outreach - View . Diversity oversight integrated within Key Risk Indicator (KRI) frameworks - View .

Actionable Strategies You Can Apply

Start by mapping CTQs and setting QTLs with statisticians - View . Limit QTLs to five to seven to preserve focus - View . Assign a risk manager to author the monitoring plan - View . Trigger monitoring visits from data signals, not calendar rules - View . Standardize on SDTM so analytics plug into every study - View . Use a shared RStudio server to scale cheaply - View . Refresh signals quarterly to balance novelty and workload - View . Summarize context so reviewers act without digging endlessly - View . Add audit‑trail visualizations into central‑monitoring workflows - View . Combine ECOD and dbScan for robust ePRO outlier scoring - View . Mirror regulator site‑inspection logic using logistic regression - View . Integrate diversity thresholds directly into RAC and KRI dashboards - View . Prioritize sites using an SDOH‑based diversity index - View . Co‑create deliverables with monitors and data managers - View .

Notable Tensions / Open Questions

Audit‑trail content remains fragmented across multiple systems - View . Teams still want machine‑readable exports baked into tools - View . The sheer log volume challenges manual and statistical review - View . Fraud detection can pressure blinding in ongoing trials - View . Organizations struggle to measure RBQM’s impact consistently - View . Remote access to vendor data remains a bottleneck - View . Smaller sponsors face resource barriers for audit‑trail parsing - View . Stakeholders question AI reliability without clear governance - View . Public‑data uncertainty threatens SDOH model sustainment - View . Sponsor‑CRO process clashes still complicate unified oversight - View .

Quantitative Snapshots

The Story in Numbers
Snapshot 1 Data Integrity Risks Demand Action
Trials fail, research is retracted, and sites pay the price when data quality and transparency falter.
40 % Phase 3 failure causes ≥40% of Phase 3 trials fail for reasons unrelated to efficacy Highlights that operational/quality failures eclipse biology, framing the central need for data integrity and RBQM.
Summary · View
10000 papers Research retraction surge (2023) Number of research papers retracted in 2023 (record) Demonstrates unprecedented scale of research integrity problems, motivating active data integrity surveillance.
Summary · View
52 % Sites self-funding recruitment costs Share of research sites paying recruitment from their own budgets without reimbursement (reported as 52–53%). Core data point repeated as unacceptable; underpins the call for flexible funding and hybrid central/local recruitment models.
Summary · View
Snapshot 2 Modernization Accelerates Trial Efficiency
Advanced tools and approaches reduce burdens, improve data-handling, and shorten timelines across study phases.
2 years Timeline reduction with insights Average phase II–III development time reduction after integrating patient insight at Lilly Direct, portfolio-level impact linking patient-voice integration to substantial acceleration of development timelines.
Summary · View
60 % Automated query resolution rate Share of manual data queries auto-resolved in platform test run Directly quantifies workload reduction and cycle-time impact from the AI-enabled approach.
Summary · View
2 x eSource data-entry efficiency gain Early eSource data indicate potential 2× improvement in data-entry efficiency Suggests substantial throughput and quality benefits from eSource, supporting the digital transformation case.
Summary · View
Snapshot 3 Risk-Based Strategies Gain Ground
Organizations adopt RBQM and early engagement, showing strong value in quality management and trial outcomes.
50 % RBM/RBQM adoption rate Share of new studies employing RBM/RBQM concepts (Tufts, late 2023) Demonstrates mainstream adoption of RBM/RBQM, directly supporting the talk’s thesis on readiness for AI-driven, risk-based approaches.
Summary · View
88 % SOPs mandate risk assessment Organizations with SOPs mandating risk assessment in Phase 1–3 trials Demonstrates unexpectedly high maturity of formal risk assessment practices across core clinical phases.
Summary · View
95 % Early-risk engagement value Stakeholders saying early-risk activity produced valuable output & was well moderated Directly quantifies perceived value of the early cross-functional risk process, central to the talk’s core claim of benefits from early engagement.
Summary · View

Connected Themes

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Presentation Summaries

Summaries, takeaways, links to recordings

Date: 2025-02-04 11:05

RBQM Revealed: A Decade of Discovery & the Blueprint for Tomorrow’s Breakthroughs

  • Joanne Benedict, Senior Director, Clinical Operations, Head, Risk Based Quality Management, Gilead
Full Recording

Key Takeaways

  • RBQM adoption typically takes a decade. View
  • Right-sized KRIs prevent alert fatigue. View
  • Open-source collaboration accelerates R3-ready analytics. View

Summary

This session reviews a decade of RBQM progress, distilling what consistently works and what stalls adoption. It contrasts blanket monitoring with targeted oversight built on timely central review and risk-driven planning. It surfaces structural blockers across data access, operating model, and measurement, then outlines a practical blueprint for the next phase with automation and industry alignment. The goal is faster follow-through with visible accountability across functions. View

Governance moves from policy to practice by mandating RBQM with clear KPIs and cross-functional champions. Risk mitigations pull through into functional plans so actions persist beyond the RACT, while documentation standards maintain inspection readiness. The organizational home for central monitoring stays flexible, but accountability is explicit and measured. This operating cadence keeps quality intent tied to day-to-day execution. View

Central monitoring depends on near-real-time data, yet vendor access and fragmented feeds still slow review. Teams avoid duplicating on-site workflows in remote form and automate risk pull-through between systems to reduce manual effort. Focused cleaning on critical data limits churn, while AI-driven signal detection emerges as the primary efficiency lever. The result is fewer cycles of rework and a smaller on-site footprint without sacrificing oversight. View

1

Date: 2025-02-04 11:55

Critical-to-Quality Factors (CTQs)—Beginning with the End in Mind

  • Cilla Mistry, Central Monitoring Process Manager, Central Monitoring and Data Analytics, GSK
Full Recording

Key Takeaways

  • Limit QTLs to five to seven per study. View
  • QTLs are study-specific. View
  • CTQs require ongoing review after startup. View

Summary

CTQs defined early and embedded in RBQM shape study design and ongoing oversight. The session positions CTQs within QBD planning and aligns them with QTLs set with statistical input. Targeted visualizations and central monitoring translate risk into concrete actions. Case studies show how CTQs for endpoint labs, oncology eligibility, and vaccine safety flow through processes and data. The aim is proactive quality control that reduces inspection burden. View

A clear operating model places a central monitoring lead at the core, with domain experts co-owning CTQs across functions. Governance artifacts such as RACT entries, QTL listings, and central monitoring plans make risks explicit and traceable. CTQs permeate study roles, ensuring that process owners, data owners, and monitors share accountability. An integrated review plan is still maturing, but the current structure already enables coordinated oversight. View

Analytics instrumentation operationalizes quality goals. Dashboards monitor predefined thresholds and surface breaches so teams act quickly. Visuals focus attention on critical data and processes without flooding users. This setup supports timely interventions and visible accountability. View

2

Date: 2025-02-04 12:15

Right From the Start: Story on How Risks Identified at the Study Design Phase Foster Cross-Functional Early Engagement and Pay Off during the Study Conduct Phase

  • Maha Raheb, MD, Associate Director, Risk-Based Quality Management, AstraZeneca
Full Recording

Key Takeaways

  • Design-phase RBQM cuts protocol amendments and rework. View
  • Signal-based monitoring replaces routine visit schedules. View
  • Concept-stage risk triage drives decisive, high-quality protocols. View

Summary

AstraZeneca Canada outlines a portfolio pilot that embeds RBQM at protocol concept to align with ICH E6 R3. Cross-functional teams convene early to run initial risk discussions, prioritize critical data, and set the trajectory for downstream oversight. The approach unifies scientific design with operational quality from day one to prevent avoidable friction during conduct. The program aims to institutionalize design-integrated quality that speeds follow-through and strengthens visible accountability. View

Governance shifts upstream with a formal initial risk discussion embedded in concept reviews and a dedicated RBQM ambassador role that shapes decisions early. Quality partners inject audit trends and regulatory insights, turning historical weak points into preventive controls. Standardized due diligence questionnaires for vendors, AI, and computerized systems expose compliance gaps before commitments. The result is clear risk ownership and a predictable decision path without slowing design. View

Operationalization centers on bespoke monitoring plans authored by the risk manager, maintaining a direct line from prioritized risks to oversight activities and analytics. A critical-data mindset takes hold across teams, which simplifies focus and clarifies responsibilities. Post-implementation feedback highlights stronger alignment to RBQM strategy and high satisfaction with CM support, validating the model at scale. The outcome is a repeatable workflow that scales across portfolios while preserving clear accountability. View

3
ZS Associates Inc logo Sponsored by ZS Associates Inc

Date: 2025-02-04 12:35

Patient Voice as the Next Frontier in Trial Quality

  • Jonathan Rowe, Principal & Head, R&D Quality Operations & Risk Mgmt, ZS Associates Inc
  • Laura Engerman, Principal & Head of Site & Patient Engagement, R&D, ZS Associates
  • Katherine Broecker, Senior Director, Data Insights, Patient Engagement, Eli Lilly & Co
Full Recording

Key Takeaways

  • Patient voice is now an audited element of clinical quality. View
  • Quantified patient and site burden improves retention and reduces deviations. View
  • AI-enabled federation of patient-experience data unlocks real-time, scalable insight. View

Summary

The session maps how to institutionalize patient voice across design, RBQM, and operations at scale. It targets gaps sponsors identified: reach lay patients beyond advocacy groups, expand global representation, speed access, and enable repeatable insight generation. The approach pairs an industry-shared Patient Experience Bank with rapid survey channels and practical case studies that contrast slow advisory boards with high-velocity methods. Intended outcomes are faster recruitment, better adherence, fewer deviations, and shorter cycle times. The goal is reliable, enterprise-grade patient centricity that sustains predictable delivery. View

Lifecycle governance shifts to prevent preventable failures. Teams embed patient involvement checkpoints at risk assessment kickoff, document input in protocols, and use ethics-ready language in participant materials. A pediatric colonoscopy study that stalled under unacknowledged caregiver burden underscores the cost of skipping these steps. The process creates traceability that de-risks design choices and signals accountability. View

Methods shift from slow advisory boards to scalable, structured instruments. A shared Patient Experience Bank uses a 170-item survey to capture travel tolerance, device literacy, and support needs, enabling rapid segmentation by country or subpopulation. Insights like higher visit-duration tolerance in Brazil inform visit schedules, while lightweight A/B testing apps pressure-test perceived complexity before SIV. These tools produce reproducible evidence that teams can reuse across programs. View

4
Saama logo Sponsored by Saama

Date: 2025-02-04 13:05

 RBx: Empowering Study Conduct Teams with Seamless Process and Technology Integration

  • Aamir Jaka, VP Life Science Strategy, Commercial, Saama
Full Recording

Key Takeaways

  • Unified data and tooling is the foundation for RBQM at scale. View
  • Cross-functional risk planning moves mitigation earlier. View
  • Agentic AI sets monitoring intensity dynamically. View

Summary

This session charts the shift from pilot RBQM to a durable operating model as adoption accelerates. It addresses the infrastructure and workflow gaps that stall ROI, driven by recent technology advances and broader access across sponsor sizes. The approach combines process modernization, analytics-first oversight, and automation aligned to ICH E6 (R3). The intended outcome is faster decision cycles, visible accountability, and a lighter inspection footprint. View

Teams pivot from traditional data management to a clinical data scientist mindset. Natural-language and code-generation tools let clinicians and monitors probe complex questions without engineering bottlenecks. This capability shortens the insight loop but demands significant training and SOP updates. Human oversight remains essential as organizations recalibrate roles and governance around AI-enabled review. View

Critical data tagging elevates endpoints and key variables so systems track lineage across sources and surface deviations quickly. Joint review hubs for data management and medical monitoring align interpretations and speed follow-up on high-value signals. The remaining frontier is credible mitigation guidance at the site level, which requires robust behavioral datasets that few possess today. Progress here unlocks more precise resource allocation without inflating oversight burden. View

5

Date: 2025-02-04 15:25

Integrating Data Science at-Scale: Lessons Learned in RBQM and Anomaly Detection for Clinical Operations

  • Numan Karim, MS, Associate Director, Data Science & Analytics, AbbVie, Inc.
  • Alicia Worrall, Associate Director, Centralized Monitoring and TA Analytics, Data & Statistical Sciences, AbbVie, Inc.
Full Recording

Key Takeaways

  • SDTM-first architecture enables enterprise-scale RBQM analytics. View
  • Portfolio-wide anomaly detection completes in hours. View
  • A unified signal layer links KRIs, QTLs, anomalies, and LLM next-best actions. View

Summary

This session outlines how a large RBQM program operationalizes data science at scale within clinical operations. It focuses on equipping study teams with portfolio-level quality signals through standardized inputs, lightweight tooling, and pragmatic refresh cycles. The approach emphasizes co-creation, change-management, and an integrated review experience that keeps outputs interpretable and actionable. The outcome is a sustainable service that drives faster follow-through and visible accountability across studies. View

Operational cadence is deliberate. Quarterly refreshes provide enough signal novelty while avoiding churn, and teams confirm that weekly runs add noise without value. Pre-curated summaries land with the right level of context so reviewers can rapidly triage and move on. This rhythm reduces cognitive load and sustains adoption over time. View

Signal governance recognizes that anomalies are prompts, not findings. Review flows privilege an integrated, portfolio context so patterns emerge before escalation, and summaries minimize data hunting. Co-creation with monitors shapes thresholds, language, and disposition notes that fit real workflows. This keeps the model credible while reducing false-positive friction. View

6
Nucleus Network logo Sponsored by Nucleus Network

Date: 2025-02-04 15:55

Quality at the Source

  • Graham Wood, CSO, USA, Nucleus Network
Full Recording

Key Takeaways

  • eSource adoption halves transcription effort and strengthens early quality signals. View
  • Structured protocol reviews cut amendment rates by 15%. View
  • AI-assisted categorization accelerates detection of recurring quality issues. View

Summary

This session reframes trial quality around engineering it at the source to reduce reliance on heavy post-hoc monitoring. It covers a cross-functional operating model spanning protocol design reviews, closed-loop site workflows from source to EDC, and an EQMS-plus-AI backbone for early trend surfacing across a high volume of FIH work. It contrasts the hidden cost of remediation with the practical realities of hybrid paper-to-digital operations and near-term data-flow automation opportunities. The discussion positions upstream investment as the lever for durable quality and operational velocity. The goal is faster follow-through, visible accountability, and a lighter inspection burden. View

Workflow design centers on a closed-loop from floor to EDC that resolves issues at the visit. Monitoring shifts toward confirmation rather than discovery, aiming for a quiet, “Maytag” experience with little to escalate. Continuous incident capture and rapid correction compress the observation-to-fix cycle. This rebalances oversight toward prevention and reduces noise in downstream review. View

Change management proceeds in stages, with hybrid paper-digital workflows governed to avoid process drift. Adopting a sponsor’s one-off eSource for a single study disrupts SOPs and elevates error risk, so standardization is prioritized. Teams report a smooth transition while exploring automated EMR-to-EDC transfer as the next step. This approach balances modernization with compliance and operational stability. View

7

Date: 2025-02-04 16:45

Elevating Quality Management through Analytics

  • Kevin Richards, Head, Quality Investigations & Analytics, AstraZeneca
Full Recording

Key Takeaways

  • Crawl-walk-run analytics maturity accelerates adoption while reducing risk. View
  • Regulator-aligned logistic regression ranks site inspection risk and hardens compliance. View
  • GenAI topic modeling turns quality text into actionable categories. View

Summary

An enterprise program elevates quality management by centralizing study oversight, standardizing risk-based metrics, and embedding analytics into daily operations. The approach uses a consolidated portal, automated inspection-readiness workflows, and targeted AI applied to well-scoped use cases. Emphasis stays on data quality, transparency, and fit-for-purpose methods teams trust. The goal is faster follow-through, visible accountability, and a materially reduced inspection burden. View

Workflow modernization centers on the SQO portal that unifies cross-functional modules and moves quality signals into the flow of work. Dashboards refresh daily to expose completeness gaps, while alerts post directly into Teams channels to prompt action. An automated study storyboard replaces sprawling spreadsheets to standardize how studies present risk and mitigation context. The result is tighter cycles from signal to decision and less manual reconciliation. View

Governance focuses on right-sizing analytics and making outputs explainable. Teams default to transparent diagnostic analysis when it answers the question, reserving ML and NLP for defined buckets such as volume prediction, risk probability, cause–effect, and document summarization. This stance directly addresses the recurring trust question while reinforcing data hygiene and stakeholder confidence. It creates a pragmatic on-ramp for AI that protects credibility and value. View

8

Date: 2025-02-04 17:05

Analytics for Automated Outlier Detection of ePRO Data: Ensuring Data Integrity and Quality in Clinical Trials

  • John Samuelsson, PhD, Senior Data Scientist, Artificial Intelligence & Machine Learning Quantitative & Digital Sciences, Pfizer Inc.
Full Recording

Key Takeaways

  • Composite ECOD+DBSCAN risk scoring reliably detects compromised ePRO sites. View
  • Audit-trail completion-time decay is a powerful integrity signal. View
  • Databricks integration enables real-time, scalable RBQM analytics. View

Summary

This session introduces a data-driven complement to threshold-based ePRO review within centralized monitoring. It outlines an unsupervised pipeline that ingests e-diary feeds, engineers site-level features, and produces site risk scores for targeted follow-up. The work tests across vaccine trials and synthetic integrity scenarios to probe robustness and portability. It targets operationalization within RBQM with minimal study-specific tuning. The goal is sharper risk prioritization and faster follow-through. View

Feature engineering converts mixed-response diaries into analyzable vectors, preserves continuous scales, and explicitly encodes missingness without imputation. A custom distance metric handles sparsity so inter-subject comparisons remain stable. Site-level descriptors summarize variation, dispersion, bias, and response dynamics across subjects. ECOD tail scores pair with DBSCAN density signals in a single site risk. View

Adoption hinges on clear guardrails: the model flags unusual patterns rather than asserting fraud, and human review decides disposition. Sensitivity diminishes as high-integrity subjects dominate, which argues for continuous monitoring thresholds and triage policies. Heterogeneous audit-trail schemas across vendors complicate portability and require mapping standards before scale-up. The effort remains experimental, with broader validation and oversight needed to align with RBQM governance. View

9

Date: 2025-02-04 17:25

Advancements and Challenges in Clinical Trial Data Quality Control: A Roadmap for Audit Trail Analysis

  • Olgica Klindworth, Vice President, Data Quality and Risk Management Solutions, Medidata a Dassault Systemes Co.
  • Jennifer Krohn, MS, Associate Director, RBQM, Clinical Operations, Gilead Sciences. Inc.
  • Kevin Stephenson, MBA, MS, Executive Director, Data Management, Karyopharm Therapeutics
  • Simon Walsh, Head, Data Acquisition and Coding, Johnson & Johnson Innovative Medicine
Full Recording

Key Takeaways

  • Audit-trail analytics moves to the frontline of RBQM and inspection readiness. View
  • Demand machine-readable, product-embedded audit trails across vendors. View
  • Apply AI to uncover unknown risk signals in massive logs. View

Summary

This session maps a practical roadmap for audit-trail analysis within modern RBQM programs. It frames the problem across exploding volumes, multi-system heterogeneity, and the shift to patient-generated data. The discussion centers on CTQ-driven focus, ICH E6(R3) proportionality, and closer QA and operations alignment. It translates those principles into workable review plans and adoption steps for sponsors of all sizes. The goal is reliable, scalable oversight that delivers faster follow-through and reduced inspection burden. View

Governance emphasizes coordinated execution between QA, central monitoring, and data management around CTQ risks. Teams set review cadence and scope using ICH E6(R3) proportionality, then define triggers, escalation paths, and handoffs. A collaborative operating model replaces ad hoc task forces with sustained routines and shared backlogs. Training on the vendor-specific “audit-trail language” tightens interpretation and shortens resolution cycles. View

Data operations tackle fragmented systems, hidden feeds, and millions of transactions per study. The path forward favors standardized, cross-system aggregation with role-based views that surface provenance and change history without manual parsing. Intuitive UI and visual telemetry reduce cognitive load and make results defensible in site and inspection settings. Cost-effective, turnkey options keep smaller sponsors compliant as scale increases. View

10
Datacubed Health logo Sponsored by Datacubed Health

Date: 2025-02-05 08:15

Digital Innovation’s Role in Pediatric and Elderly Clinical Trials

  • Kyle Hogan, CEO, Datacubed Health
Full Recording

Key Takeaways

  • RBQM starts with real patient and site problems, not gadgets. View
  • Adaptive reinforcement replaces static reminders to maintain data quality. View
  • Every protocol requires study-level validation. View

Summary

The session aligns digital innovation with the realities of pediatric and elderly trials. It examines why prior gadgets underperform in the field, then sets a pragmatic mobile approach shaped by behavior science and site workflow. Case examples across pediatrics, oncology, and early dementia show how engagement, remote tasks, and everyday data entry translate into usable endpoints. The narrative emphasizes practical deployment and study-specific tailoring that lead to measurable follow-through. The overarching goal is faster follow-through, visible accountability, and reduced inspection burden. View

Usability is treated as an operational discipline with pillars that keep teams focused on what matters. Simple reports surface essential metrics, while flexible configuration adapts to study and site differences. Design choices draw from deep end user insight to minimize friction at critical moments. This discipline turns initial adoption into sustained use that yields cleaner, timelier data. View

Rare-disease work demonstrates how remote cognitive tasks expand sampling without travel. In frontotemporal dementia, frequent at-home assessments still deliver meaningful early signals. The approach suits populations that struggle with site visits and supports broader representation without inflating burden. Early feasibility results position the model for scaling across pediatric and elderly cohorts. View

11
Sanofi logo Sponsored by Sanofi

Date: 2025-02-05 08:15

Scaling Success: How Sanofi and Trialbee Drive Patient-Centric Recruitment and Reduce Site Burden in Global Asthma Programs

  • Whitley Albright, Clinical Innovation and Operations Strategy Lead, Sanofi
  • Gaynor Anders, Chief Delivery Officer, Trialbee
Full Recording

Key Takeaways

  • Programmatic recruitment scales across multi-compound pipelines. View
  • Two-step pre-screening delivers only doubly qualified referrals to sites. View
  • Public patient-engagement KPIs elevate accountability. View

Summary

Session lays out a patient-centric recruitment operating model for global asthma programs. It aligns real-time analytics, privacy-by-design data flows, and registry-based opt-ins to create a repeatable way of working. The approach prioritizes site experience through clear feasibility expectations and co-developed workflows that keep operational friction low. It balances speed with quality through risk-based decisioning as study complexity rises. The goal is faster enrollment and lower site burden. View

Operational success hinges on disciplined site engagement. Teams reduce technology sprawl and set candid expectations at feasibility to avoid rework. They institutionalize feedback through SPFQ and site panels, then fold insights into tooling and workflow roadmaps. Early cross-functional alignment on procurement and roles improves throughput and strengthens site trust. View

Governance scales with the program through layered oversight and privacy agility. Policies update quarterly to track regional changes, while RBQM guides which risks to monitor and mitigate. Consent, data minimization, and controlled sharing shape registry and pre-screen data flows. This produces inspection-ready evidence without slowing recruitment. View

12

Date: 2025-02-05 09:15

Leveraging RBQM Technologies to Achieve Diversity Action Plan Goals

  • Damalie Akuamoah, Diversity Program Lead, Merck
  • Naveen KK, Vice President & Global Head, CMR, CM & Safety Services, Fortrea
  • Lydia Matombo, Director, Risk Evaluation & Adaptive Integrated Monitoring, Merck & Co., Inc.
Full Recording

Key Takeaways

  • KRI and QTL adaptation hard-wires DE&I into RBQM. View
  • Heat map triggers activate earlier mitigation before enrollment gaps widen. View
  • Enrollment targets are set from epidemiology, SDoH, and RWE data. View

Summary

This session presents a third-year maturity update on embedding inclusive enrollment oversight into routine RBQM. The team leverages an existing Accelerate SaaS partnership rather than building new tooling, expanding oversight from startup through closeout. It aligns corporate commitments with evolving expectations and standardizes how DE&I progress is reviewed across studies. The approach tightens feedback loops and makes progress review part of daily operations. The overarching goal is visible accountability for diversity performance across the portfolio. View

Governance centers on shared workspaces and auditable decisions. DPLs and Central Monitors work in the same RBQM modules, with RAC thresholds signaling when to escalate actions. The RIM module captures day-to-day decisions and rationales to create a clean evidence trail for submissions. A formal diversity risk library keeps DE&I threats visible alongside traditional quality risks. View

Operationalization relies on protocol-specific subpopulation dashboards and simple visual cues. A North Star trendline shows whether actual enrollment tracks targeted proportions without overcomplicating analysis. When gaps emerge, the primary lever is direct site and community engagement, supported by targeted outreach and investigator coaching. The result is pragmatic course correction that preserves protocol intent while maintaining momentum. View

13
PA Consulting logo Sponsored by PA Consulting

Date: 2025-02-05 09:55

Speed and Precision in Drug Development to Deliver Unmatched Speed to Approval

  • Charlie Paterson, Associate Partner and Clinical Development Expert, PA Consulting
  • Gino Pirri, VP Product & Technology, Product & Technology, PPD Part of Thermo Fisher Scientific
Full Recording

Key Takeaways

  • Shift-left RBQM with real-time oversight shortens database lock. View
  • Gen-AI automates site queries to cut manual workload by 60%. View
  • Unified workbench pushes actions to native EDC to reduce site burden. View

Summary

An AI-enabled Intelligent Clinical Suite reorients clinical operations around continuous, risk-based quality to compress database lock and unify data. The scope spans real-time integrations, intelligent rule creation, and a collaborative workbench that supports global teams across large study portfolios. The approach interprets protocol intent on import and orchestrates end-to-end quality workflows from ingestion through issue prioritization. Expected outcomes include faster startup, fewer manual queries, and earlier deviation control. The goal is unmatched speed to approval with visible, data-driven quality oversight. View

Quality governance shifts from retrospective cleanup to proactive surveillance that aligns with ICH E6 R3 expectations. The program surfaces risk signals as data lands, enabling earlier deviation control and cleaner, audit-ready trails. It targets persistent pain points—fraud exposure and diversity gaps—by making anomalies and enrollment patterns transparent in near real time. The result is defensible decision-making that reduces inspection burden and accelerates follow-through. View

Legacy study data powers the learning layer, improving detection logic with each deployment. Models ingest protocol metadata and outcome history to calibrate thresholds, reduce false positives, and rank issues more precisely. Feedback from real operations loops into retraining, so rule coverage and query clarity steadily improve. The net effect is a compounding analytics advantage that scales across sponsors and therapeutic areas. View

14

Date: 2025-02-05 11:25

Best Practices for Study Risk Assessment

  • Rachael Geedey, Director, Customer Success, Cluepoints
  • Kristin Stallcup, MS, Director, RBQM Operations, Takeda
Full Recording

Key Takeaways

  • Anchor risk assessment on CTQ factors. View
  • Cross-functional participation drives assessment quality. View
  • Integrate patient and site feedback into the risk log. View

Summary

An industry working group under FUSE is defining best practices for study risk assessment aligned to ICH E6 R3. The effort centers on a concise white paper that standardizes approach and clarifies roles across sponsors and CROs. Inputs include a cross-company survey and ongoing biweekly workshops to surface gaps and practical fixes. The goal is harmonized, CTQ-driven assessments that accelerate follow-through and reduce inspection burden. View

Procedural maturity is high, but execution still lags. Many sponsors operate under formal SOPs and use dedicated RBQM facilitators, yet teams struggle to turn risk evaluation into concrete monitoring actions. Maintaining the risk log consumes substantial effort at study start and throughout conduct, which increases the need for leaner workflows. Closing this maturity versus execution gap requires tightening the path from risk signal to action and simplifying upkeep. View

Governance alignment emerges as a priority, especially where sponsor and CRO processes collide. Parallel risk frameworks erode unified oversight and slow decision cycles, so the initiative focuses on process harmonization rather than tooling or templates for now. Regular cadence keeps progress moving while inviting broader participation from smaller sponsors to pressure-test scalability. The near-term aim is a single, fit-for-purpose process that travels cleanly across partnerships. View

15
Medidata a Dassault Systemes Co logo Sponsored by Medidata a Dassault Systemes Co

Date: 2025-02-05 12:25

360° Monitoring: A New Approach to Dynamic Clinical Oversight Using Centralized Insights

  • Olgica Klindworth, VP, Data Quality & Risk Management Solutions, Data Quality & Risk Mgmt Solutions, Medidata a Dassault Systemes Co
  • Lauren Price, Director, CTMS Product Management, Product Management CTMS, Medidata a Dassault Systemes Co
Full Recording

Key Takeaways

  • Digitized protocol becomes the source of truth for automated risk assessment. View
  • AI-driven site selection replaces manual mining for smarter feasibility. View
  • EHR-to-EDC integration makes zero SDV achievable. View

Summary

This session outlines a 360-degree monitoring model that links protocol intent, risk controls, and operational oversight in one continuous loop. It scopes end-to-end orchestration across central analytics and on-site execution with responsive signal detection. The approach uses historical intelligence and automation to cut manual tracking while keeping expert judgment in the loop and closing cross-functional gaps. The goal is faster follow-through and visible accountability across study operations. View

Operationalization centers on an integrated quality planning workflow that translates prioritized risks into shared, risk-weighted actions. Notifications tied to risk changes replace dashboard hunting and cue targeted follow-up. Central analytics bulk-draft findings from KRIs, QTLs, and CSM outputs, enabling teams to validate rather than create. These signals continuously tune visit cadence and focus to improve coverage without extra cycles. View

Data governance underpins cross-trial learning. Normalization across indications makes models and metrics transferable without bias. Standardized assets feed central analytics and enable mid-study pivots while preserving traceability. This discipline turns experience into reusable practice and reduces rework across portfolios. View

16
Parexel International logo Sponsored by Parexel International

Date: 2025-02-06 07:45

What Sites Really Need to Deliver Successful Patient Engagement Strategies

  • Leslie Ives, Senior Director, Patient Recruitment, Patient Strategy and Insights, Parexel International
  • Brittany Harvey, Clinical Project Mgr, Clinical Operations, UCB Inc
  • Jimmy Bechtel, Vice President, Site Engagement, Society for Clinical Research Sites
Full Recording

Key Takeaways

  • Hybrid recruitment aligned to site maturity outperforms one-size-fits-all. View
  • Structured, safe feedback loops with sites surface risks early. View
  • Self-funded site recruitment hides schedule risk. View

Summary

The session examines what sites actually need to deliver effective patient engagement as sponsors modernize trial operations. It contrasts operating approaches, surfaces real-world signals on site burden, and explores governance and tooling options. Discussion threads include vendor coordination, decision-making discipline, and the near-term role of AI in feasibility. The aim is to turn fragmented outreach into predictable enrollment with lower operational risk. View

AI enters the workflow as a research assistant that accelerates evidence scans and candidate matching. The team stresses explicit validation, traceability, and human-in-the-loop review for every output. Early use cases sit inside controlled pilots with clear guardrails, rather than replacing proven site processes. The objective is speed with accountability, not automation at the expense of quality. View

Cross-organization collaboration remains the rate limiter for recruitment execution. Rigid silos across sponsors, CROs, and vendors block real-time problem-solving and slow course correction. A recurring, agenda-light forum with fast decision rights and budget flex points enables joint action on site signals. Stronger governance shortens decision cycles and reduces unproductive spend. View

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Egnyte Inc logo Sponsored by Egnyte Inc

Date: 2025-02-06 09:40

Accelerating Clinical Success: Egnyte's Unified Platform for Data Governance and Secure Collaboration

  • Catherine Hall, Head of GXP Quality Assurance, Sales, Egnyte Inc
Full Recording

Key Takeaways

  • E6 R3 makes data governance mandatory. View
  • RBQM depends on authoritative sources and end-to-end traceability. View
  • Design site-sponsor workflows to withstand ransomware and email outages. View

Summary

The session translates rising regulatory expectations into a pragmatic operating model for clinical data. It surfaces recurring pitfalls like accidental unblinding, visit-date conflicts, and audit-trail disputes, then outlines policy, inventory, and platform options to address them. It extends stewardship across sponsors, sites, and vendors, with cloud-agnostic controls to unify sources and protect PHI. The outcome is secure, trusted, reuse-ready study data that speeds risk decisions and inspection prep. The goal is visible accountability across the trial ecosystem. View

Regulatory expectations now stress transparent auditability across parties. Investigators receive unaltered audit trails straight from systems, with vendors proving delivery and no sponsor manipulation. Governance ownership spans sponsors and sites, so responsibilities and access paths are explicit before inspections. This posture shortens evidence retrieval, reduces dispute risk, and increases confidence in system reliability. View

Technology accelerates execution once policy exists. A unified, cloud-agnostic platform aggregates sources, normalizes access, and applies role-based visibility. AI locates PHI and other sensitive content at scale to reduce accidental disclosure risk and speed remediation. Cross-industry practices, especially from finance, inform security baselines while keeping governance ownership inside the organization. View

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Date: 2025-02-06 10:10

The Many Faces of Clinical Data Integrity

  • Bartosz Wylot, PhD, Associate Director, Risk Based Quality Management, AstraZeneca
Full Recording

Key Takeaways

  • Fraud and misconduct at sites are the primary integrity risk. View
  • Centralized monitoring reveals fabrication via pattern-based signals. View
  • Regulators independently analyze metadata to detect unexpected patterns. View

Summary

This session reframes clinical data integrity as a multi-dimensional risk that outpaces classic CTQ-focused design. It connects RBQM, evolving ICH expectations, and integrity analytics into a continuous, study-long surveillance model. It explains how centralized monitoring, cross-dataset checks, and predefined escalation paths operate as one control system. It also stresses decision-making that preserves blinding while enabling rapid, proportionate action. The aim is faster detection-to-action cycles that protect readouts and sustain regulatory confidence. View

Governance centers on safeguarding blinding while running sensitive integrity analytics. Access to patient-level efficacy signals stays tightly limited, with clear rules for when and how data can be viewed. Central reviews separate detection from operational decisions to minimize bias and procedural risk. These controls enable robust detection without undermining the credibility of trial conclusions. View

Signal handling follows a defined escalation path from central detection to study-team triage and site-level action. Responses include targeted retraining, focused source review, corrective data capture, or audits based on signal strength and context. Teams then verify resolution through study-level metrics to confirm risk reduction without disrupting the broader program. This disciplined loop turns anomaly detection into timely, proportional remediation. View

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Date: 2025-02-06 11:55

Applying Social Determinants of Health (SDoH) in Clinical Study Planning and Execution

  • Daoying Hu, PhD, MBA, Director, Data Science and Digital Health, Johnson & Johnson Innovative Medicine
Full Recording

Key Takeaways

  • ML diversity index with SDOH doubles diverse enrollment at prioritized sites. View
  • Default aggregation at hospital service area resolves fragmented SDOH geographies. View
  • Regulators expect quantified diversity plans. View

Summary

Centralized data science operationalizes SDOH across trial planning and execution to make inclusion measurable and actionable. The team applies equity analytics from pre-study landscape reviews through protocol tuning and site strategy, then monitors outreach during enrollment. Tools include a diversity plan dashboard and models that elevate non-medical context alongside traditional KPIs. The approach aligns scientific rigor with regulatory expectations and field realities. The goal is reproducible, evidence-backed diversity with faster, more confident study decisions. View

Operationalization matters as much as modeling. Self-serve dashboards give clin-ops and medical leaders rapid demographic insight without analyst bottlenecks, while layered heatmaps target outreach and track response. Budget logic incorporates real travel and insurance gaps to pre-plan vouchers and support. Embedding these AI-driven assets into routine governance keeps actions timely and auditable. View

Data stewardship underpins the entire strategy. Multiple public and commercial sources vary in granularity and methodology, so teams reconcile discrepancies through method review and documented assumptions. They actively monitor policy shifts that could remove key U.S. public datasets, though mitigation paths are still under evaluation. The emphasis is durable pipelines that withstand source volatility and maintain trust. View

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Zelta by Merative logo Sponsored by Zelta by Merative

Date: 2025-02-06 13:30

LUNCHEON PRESENTATION: From Sync to Swim: Alimentiv’s Journey with Zelta ePRO

  • Wes Fishburne, Principal Product Manager, Zelta, Zelta by Merative
  • Chris Walker, Director of Data Sciences, Alimentiv
Full Recording

Key Takeaways

  • Embedded ePRO within EDC eliminates integration risk. View
  • Front-loaded ePRO sprint removes critical-path risk. View
  • Data managers own ePRO delivery. View

Summary

Alimentiv and Merative outline a Zelta ePRO program for GI trials. The session spans objectives for RBQM, patient usability, and timely analytics, and shows how configuration, localization, and deployment playbooks support those aims. The approach emphasizes native modules, controlled translation workflows, and clear governance to keep delivery predictable. Use cases demonstrate eligibility composites and protocol-driven safety diaries that feed immediate monitoring and decisions. The overarching goal is near-real-time oversight and faster follow-through. View

Integrated capture enables composite endpoints within the EDC. Eligibility calculations combine ePRO inputs such as stool frequency with site-entered measures to drive risk-based decisions. Safety diaries record protocol-mandated temperature checks with low-burden prompting, keeping data timely for RBQM signals. Near-real-time availability supports adaptive actions when thresholds trip. View

Governance shifts from vendor oversight to platform controls that meet ICH E6 R3 expectations. Teams toggle modules like medical coding with AI, local labs, randomization, and eConsent to apply fit-for-purpose risk controls. A single compliant EDC reduces audit load while localization runs through a controlled translation workflow. The net effect is clearer accountability and lower inspection risk. View

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ClinEco Vendors

ClinEco Vendors Working in Alignment With This Report’s Themes
ArcheMedX
Charlottesville, Virginia, United States
The Ready platform predicts and improves site and team performance by analyzing training interaction data, flagging study-level risks before activation. This performance-based risk insight supports the ‘early engagement, empower the risk manager’ principle emphasized at SCOPE.
View on ClinEco
Clinion
Austin, Texas, United States
Clinion’s AI-enabled eClinical platform unifies EDC, CTMS, RTSM and ePRO with built-in analytics for protocol adherence and risk flags. Its integrated architecture helps small and mid-size teams execute RBQM without stitching together point solutions, supporting the ‘lightweight, scalable analytics’ vision from the stream.
View on ClinEco
eClinical Solutions
Mansfield, Massachusetts, United States
The elluminate clinical data cloud ingests multi-source data, runs advanced analytics and offers RBQM dashboards, giving study teams real-time KRIs and audit-ready traceability. This aligns with leaders’ call to fix data plumbing and use embedded analytics to cut noise and sharpen action.
View on ClinEco
Revvity Signals Software
Waltham, Massachusetts, United States
Revvity’s Clinical Spotfire modules deliver interactive RBM/RBQM, medical review and risk dashboards on top of aggregated trial data. The platform helps ops leaders move from static listings to continuous, visual risk surveillance—exactly the pragmatic analytics maturity discussed in the SCOPE stream.
View on ClinEco