Everest Group Tech Provider Spotlight: Risk-Based Quality Management (RBQM)
Focus on eClinical Solutions
November 2025
Background of the research
The growing complexity of clinical trials, driven by hybrid models, diverse patient populations, and expanding data sources, has created significant challenges in ensuring quality, compliance, and patient safety. Traditional trial oversight approaches, heavily reliant on 100% Source Data Verification (SDV) and frequent on-site visits, are resource-intensive, costly, and often reactive. These methods can delay detection of issues, compromise efficiency, and increase trial timelines.
To address these inefficiencies, Risk-based Quality Management (RBQM) has emerged as a strategic framework. RBQM emphasizes proactive risk identification and mitigation across the trial life cycle, integrating principles such as Quality by Design (QbD), continuous risk assessment, and real-time monitoring. This approach not only improves data integrity and patient safety but also optimizes resource allocation by focusing oversight on high-priority risks.
Regulatory bodies, such as Food and Drug Administration (FDA), European Medicines Agency (EMA), and International Council for Harmonization (ICH), have strongly endorsed RBQM practices, embedding risk-based methodologies into Good Clinical Practice (GCP) guidance. These frameworks have accelerated adoption across the industry, making RBQM an essential enabler of efficient, patient-centric, and compliant clinical trials.
The report assesses 9 leading RBQM technology providers. The report features:
ď‚— Overview, evolution, and key trends in RBQM space
ď‚— Overview of capabilities and assessment of leading technology providers in RBQM space
ď‚— Case studies demonstrating capabilities in the RBQM space
Scope of this report
Geography: global
Providers: CluePoints, Cyntegrity, eClinical Solutions, IQVIA, MaxisAI, Medidata, TCS, ThoughtSphere, and TRI
Domain: risk-based quality management
RBQM Overview
RBQM enables efficient, high-quality clinical trials by embedding proactive risk management, real- time monitoring, and data -driven oversight across the trial life cycle. It ensures patient safety, regulatory compliance, and resource optimization
[NOT EXHAUSTIVE]
Risk assessment and planning
ď‚— Support for industry-standard tools such as Risk Assessment and Categorization Tool (RACT)
ď‚— Configuration of Key Risk Indicators (KRIs) and Quality Tolerance Limits (QTLs)
ď‚— Pre-built and customizable risk libraries
ď‚— Historical risk pattern recognition and reuse across studies
Centralized monitoring and signal detection
ď‚— Central Statistical Monitoring (CSM) to identify outlier patterns
ď‚— Automated signal detection
ď‚— Duplicate patient detection, patient profile comparison, and data fraud flagging
Risk mitigation and action tracking
ď‚— Workflow-based assignment of mitigation plans and corrective actions
ď‚— Real-time tracking of mitigation progress
ď‚— Linking detected risks to corrective tasks
AI/ML and advanced capabilities
ď‚— Early signal detection
ď‚— Predictive risk scoring, and prioritization
ď‚— Trial scenario simulation
ď‚— NLP to analyze unstructured data
Integration of data from multiple clinical data sources Data governance, privacy, and security Dashboarding, reporting, and analytics
Data interoperabilitybased standards
Data encryption and access control
Compliance and audit trail for risk assessments
Configurable and userfriendly workflows
Implementation, support, and advisory services
Key evolving trends and imperatives shaping the industry
Integration of AI and predictive analytics
Use of machine learning and predictive tools to anticipate trial risks, improve data quality, and enhance operational decision-making
Digitization of clinical trial oversight
Transition from manual, fragmented systems to unified digital platforms that support real-time monitoring, centralized analytics, and automated risk detection
Regulatory acceleration of risk-based oversight
Global regulators (FDA, EMA, and ICH) are increasingly mandating risk-based approaches, pushing sponsors to adopt RBQM across the clinical trial life cycle
Modernized and flexible monitoring
Shift from reliance on 100% SDV to centralized and remote monitoring, supported by risk-driven analytics and automation
Convergence of RBQM and clinical data management
Sponsors are increasingly seeking integrated platforms that combine data management and RBQM, enabling seamless oversight across data flows and trial operations
Key quality challenges in traditional clinical trials
Key quality challenges in traditional clinical trials include reactive and rigid monitoring, high site burden, lack of real- time data oversight, and delays in patient safety oversight, leading to operational inefficiencies, compliance risks, and compromised patient safety
Reactive and rigid monitoring
Details
Impact on business outcomes
ď‚— Manual 100% SDV is timeconsuming and error-prone
ď‚— Inflexible protocols hinder realtime adjustments
ď‚— Increased costs and trial timelines
ď‚— Delays affect market launch and revenue
High site burden
ď‚— Excessive documentation and complex protocols
ď‚— Frequent visits burden site staff
Lack of real-time data oversight Patient safety oversight delays
ď‚— Data reviewed in batches with delays
ď‚— Lack of digital integration limits early insights
ď‚— Adverse events identified late due to slow data review
ď‚— Safety monitoring is not integrated
How RBQM can address these challenges
ď‚— Proactively identifies risks early, reducing reactive responses
ď‚— Tailors monitoring based on sitespecific risks, ensuring flexibility
ď‚— Provides continuous risk assessment for dynamic adjustments
ď‚— Lower site engagement and more deviations
ď‚— Site dropout raises costs and causes delays
ď‚— Prioritizes high-risk sites, reducing unnecessary monitoring tasks
ď‚— Allocates resources efficiently based on risk
ď‚— Simplifies reporting and documentation for site staff
ď‚— Slow decisions cause inefficiencies
ď‚— Poor visibility hampers approvals and investor trust
ď‚— Ensures real-time data collection and instant access to key metrics
ď‚— Uses predictive analytics for early risk detection
ď‚— Supports timely, data-driven decision-making
ď‚— Risk of regulatory penalties
ď‚— Compromised safety harms brand and raises litigation risks
ď‚— Continuously monitors safety metrics for early issue detection
ď‚— Focuses on high-risk safety areas for faster action
ď‚— Provides automated alerts for immediate communication of safety concerns
Evolution of RBQM
Level 4
Level 3
Level 2
Level 1 Introduction of Risk-based Monitoring (RBM)
Traditional clinical trials
Clinical trial monitoring was traditionally conducted through onsite visits with 100% SDV. This reactive and rigid approach ensured compliance but was resourceintensive, time-consuming, and often ineffective in identifying critical issues early. It resulted in high site burden, delayed oversight on patient safety, and limited real-time visibility into data quality.
RBM emerged to address the inefficiencies of traditional monitoring. It focused on prioritizing monitoring efforts based on identified risks, reducing the emphasis on exhaustive SDV. Centralized monitoring, remote assessments, and data analytics began supplementing on-site checks. However, it remained limited in scope, primarily targeting monitoring activities rather than broader quality management.
RBQM and regulatory advocacy
Recognizing the need for holistic oversight, RBQM extended beyond RBM to encompass risk assessment, protocol design, data integrity, and patient safety. Regulatory bodies such as the FDA, EMA, and ICH began actively advocating RBQM adoption, issuing formal guidelines that integrated quality by design, critical data identification, and proactive risk mitigation. This formal support catalyzed industry-wide shifts toward more efficient, compliant, and patient-centric trial operations.
Future focus of RBQM
RBQM is now progressing toward a technology-enabled, adaptive model. The integration of AI, real-time analytics, and interoperable platforms is enhancing predictive risk detection, enabling continuous oversight, and reducing operational burden. Future advancements aim to make RBQM more scalable, automated, and embedded across the clinical trial life cycle—promoting faster, safer, and more reliable research outcomes.
Important considerations when selecting RBQM products
Scope of the report: Key considerations for RBQM platforms
Technology fit and integration Collaboration, compliance, and security
ď‚— Has comprehensive RBQM functionalities throughout the risk life cycle (RACT, KRIs, QTLs, issue tracking, etc.)
ď‚— Seamlessly integrates with existing systems such as Clinical Trial Management System (CTMS), Electronic Data Capture (EDC), and electronic Clinical Outcome Assessment (eCOA)
ď‚— Handles large, global, multi-site studies without performance drops
ď‚— Enables low-code/no-code configuration without IT dependency
ď‚— Compliant with global regulatory guidelines such as ICH E6(R2), GCP, FDA, and EMA
ď‚— Has strong data security controls, meeting HIPAA, GDPR, 21 CFR Part 11 standards
ď‚— Enables role-based access and communication tools for secure collaboration across teams and geographies
Early planning and change management
ď‚— Provides customizable workflows by adapting to SOPs, approval steps, and study-specific nuances
ď‚— Supports phased implementation allowing gradual adoption to minimize disruption and resistance
ď‚— Provides comprehensive audit trails ensuring transparency, accountability, and regulatory readiness
Continuous review and improvement Awareness, knowledge, and skillset
ď‚— Provides real-time tracking of risks, metrics, and study quality through live dashboards and alerts
ď‚— Enables feedback loops from trial data, learning from past risk events to enhance future planning
ď‚— Provides integrated CAPA management, which supports timely resolution and documentation of issues
ď‚— Supports CRAs, data managers, and QA teams with role-specific training modules
ď‚— Has built-in guidance, such as tooltips, help content, and smart defaults, to reduce user error and training time
ď‚— Provides access to templates and frameworks such as preloaded risk libraries and SOPaligned workflows, which support consistency
eClinical Solutions | overview
Product overview
The elluminate Clinical Data Cloud® is leveraged by over 80 life sciences companies across the US and Europe. Clients span industry segments from midsize to large biotech and pharmaceutical companies to CROs, including 16 of the top 50 life sciences companies. The platform provides capabilities across different roles related to RBQM, data management, transformation, mapping, programming and analytics. RBQM is one of the core capabilities, allowing a broad spectrum of risk-related activities including defining strategy, dynamic monitoring of the risks, risk signals management and integration with data review.
Headquarters: Mansfield, Massachusetts
Website: www.eclinicalsol.com
Product launch date: 2021
Key leaders
ď‚— Raj Indupuri, CEO and Co-Founder
ď‚— Bob Arnesen, President and Co-Founder
ď‚— Sam Anwar, Chief Technology Officer
ď‚— Marty Roche, Chief Business Officer
ď‚— Nathan Johnson, VP, Product Management
ď‚— Vera Pomerantseva, Director, Product Management Key clients Jazz Pharmaceuticals
Partnerships
Key service providers / reseller partners Key technology provider partners
ď‚— PwC
ď‚— Halloran ď‚— Consulting Group
Cognizant
Accenture
Snowflake
AWS
Clinical ink
Medidata
Veeva
Databricks
By client base
(if any)
By buyer size Small (annual
Types of RBQM offerings
Analytics services
Not present
eClinical Solutions | capability dimensions (page 1 of 2)
Scope of the report: key considerations for RBQM platforms
Market adoption / Client base Platform capabilities
Everest Group comments
eClinical Solutions has its RBQM module as part of the broader elluminate platform. Adoption is the strongest among midsized sponsors, but it is also present among larger and smaller ones. The solution is often positioned as part of integrated clinical data management programs, making it attractive to clients seeking a combined data platform and RBQM capability.
The platform provides comprehensive RBQM capabilities, including a RACT tool with configurable risk libraries, AI-assisted risk description generation, and KRIs/QTLs configuration. The Data Central module supports monitoring with dashboards and visualizations. Risk mitigation and action tracking features are available, but with limited workflow automation.
Technology maturity and stability
Thought leadership / Market impact
The RBQM module, within elluminate platform continues to mature. eClinical Solutions maintains an aggressive product roadmap, delivering frequent updates across the broader platform and RBQM. Stability is supported by the platform’s integration with elluminate’s larger data infrastructure.
The company actively promotes its RBQM offering through webinars and whitepapers. While much of its visibility is tied to elluminate as a whole, RBQM messaging is increasingly highlighted. Thought leadership impact is growing but remains less established than that of dedicated RBQM players.
eClinical Solutions | capability dimensions (page 2 of 2)
Scope of the report: capability focus on RBQM platforms
Risk assessment and planning Centralized monitoring and signal detection
Everest Group comments
Elluminate RBQM provides a configurable RACT tool, along with structured risk libraries to capture risks consistently across studies. AI/LLMassisted functionality helps generate risk statements efficiently, reducing manual effort while maintaining compliance.
The Data Central module supports oversight of KRIs and QTLs, with dashboards and visualization tools that enable teams to track trends and emerging issues. Filtering techniques simplify navigation across datasets, allowing users to focus on high-risk areas. In addition, the eIQ Review module supports subject-level anomaly detection, flagging atypical values and outliers across datasets.
Risk mitigation and action tracking
AI/ML and advanced capabilities
The platform enables configurable workflows for linking detected risks to mitigation tasks, providing accountability and oversight. Risk registries help document issues and actions, but automation and real-time compliance alerts remain limited compared to its peers.
AI capabilities are beginning to shape the platform, with LLM-driven risk statement generation already in place. The roadmap includes extending AI/ML to anomaly detection and centralized monitoring, which strengthens predictive oversight.
eClinical Solutions | case study
Problem Solution
A midsized biopharma company using the elluminate platform wanted to adopt RBQM and RACT. The solution needed to align with existing workflows, be flexible for future changes, and avoid the additional burden of managing a separate RBQM system and migrating data.
A European biotech firm needed to implement RBQM with the ability to track risk realization in RACT and customize KRIs and QTLs to support complex study requirements. The existing risk management process required greater flexibility and customization.
The client implemented elluminate RBQM, leveraging its existing data infrastructure to enable quick deployment. This allowed risks to be assessed, identified, and mitigated without the delays and cost of migrating to an external point solution.
Results
The company implemented elluminate RBQM, which provides configurable RACT fields, options for custom algorithms, and flexible logic for KRIs and QTLs. This enabled the client to tailor the system to specific study needs while staying aligned with standard risk assessment practices.
The company avoided costly data migration and saw immediate benefits. Using RACT and RBQM across 25 studies streamlined processes, reduced manual spreadsheets, and created a single source of truth for risk review and oversight, enabling more efficient workflow management.
Elluminate RBQM allowed the biotech to track realized risks directly in RACT and apply customized KRIs and QTLs for each study. The platform supported compliance with regulations and company SOPs, while enabling teams to adapt risk monitoring strategies to trial-specific requirements.
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