oday, data managers report spending nearly 12 hours per week, per study to complete data review, cleaning, and reconciliation. These processes often rely on spreadsheets, manual work, and siloed tools that slow execution. With growing data volumes and increasing regulatory requirements, maintaining exhaustive data reviews isn’t sustainable. The clinical data review process must change.

Risk-based data management (RBDM) is the data-specific contribution to RBQM that identifies and evaluates risk for key data for a study, enables proportionality, and supports compliance. RBDM provides clinical data teams with an advanced, faster data review process; even so, confusion remains about application and execution. Even as regulation drives more risk-based strategies, these three factors challenge the successful adoption and implementation of RBDM:

1. RBDM versus RBQM: Lack of clarity about the discipline of RBDM and how it fits within the larger RBQM strategy.
2. Legacy clinical data management systems: Reliance on legacy electronic data capture (EDC)-centric architectures that can’t support targeted, risk-based workflows.
3. Gaps in change management: No organizational commitment to advance legacy processes, upskill data teams, or redefine roles.

Any disconnect in understanding how to implement RBDM within an RBQM framework can deter adoption of risk-based data review. Defining the role of each within the clinical organization can help teams get started.

Understanding the Difference between RBQM and RBDM

The overarching discipline that directs risk-based approaches for the trial from concept to close-out is RBQM. ICH E6(R3) defines risk-based as two fundamental principles that span the planning and execution phases: Proportionality (Principle 7) and QbD, defined in ICH Q8(R2). These principles are the groundwork for risk-based processes across trial design and conduct. RBQM applies these principles to the overarching cross-functional activities of the trial, while RBDM applies them to data-specific activities.

Clinical Application of RBQM and RBDM

QbD guidelines state that clinical data management involvement should be upstream from protocol design to proactively define CtQ factors, identify data risks, and collaborate with other functions to derisk the protocol. When considering study conduct, applying the principle of proportionality means that the time spent on reviewing data must be proportionate to the risk and criticality of the data collected. Proportionality places increased effort on reviewing primary and key secondary data while reducing the emphasis on resource-intensive noncritical data points, such as reconciling patient-reported adverse events (AEs) with concomitant medication dates.

Confusion within clinical teams around RBQM and RBDM often causes delays in execution. Typically, RBDM initiatives are tied into broader RBQM projects before gaining traction. This pushes data teams to put work on hold as they wait for the start of larger risk-based implementations before actioning RBDM. By prioritizing RBDM as a separate but related component of RBQM, clinical data teams can gain meaningful traction in adopting risk-based strategies.

Moving from Standalone Tools to Centralized Clinical Data Hub

The increased urgency in deploying RBDM, in part, is due to the increasing number of data sources in clinical trials. The volume of data sources used in a typical study is significantly more than it was 10 years ago, shifting EDC away from its primary data management role towards managing many data sources. To effectively execute risk-based workflows, clinical data teams can’t rely on legacy EDC-centric architectures that lack real-time integration and automation. RBDM calls for a new centralized hub for clinical data: the workbench.

A clinical data workbench ingests and manages all clinical data sources, including EDC, labs, electronic clinical outcome assessment (eCOA), and eSource. A workbench can also automate manual processes and unlock data flow across the clinical ecosystem. For data managers, the workbench frees up time for high-value tasks by flagging when data review is needed for new or changed data with automated documentation and real-time reporting.

These three pillars define the core capabilities a workbench needs to execute RBDM:

Classification: Recognizing critical data as defined by data managers. With defined data priorities, a workbench can understand the data’s meaning and purpose. This allows the system to centralize the data with an understanding of its use, allowing data managers to prioritize actions on the data that matter most. The workbench classifies critical data, endpoints, and clinical concepts, helping data managers move their focus from reviewing all data towards a more targeted, risk-based review. Classification of data in this way is in the early stages, as organizations begin exploring its opportunity and benefits.

Automation: Industry can automate approximately half of manual data management queries today, comparing collected data across sources or against expected values. If the workbench understands the data’s purpose, clinical teams can automate cleaning and reconciliation with minimal human intervention and scale it across studies. Taking this approach also requires connectivity across clinical systems to automate data flows and actions downstream. A connected clinical ecosystem provides the foundation for the next wave of automation: AI-enabled data aggregation, standardization, cleaning, and transformation.

One top 20 biopharmaceutical company prioritized refining its change-management process to reduce the administrative burden on its staff. One area of focus was reconciliation of serious adverse events (SAEs) across the safety database and EDC, which traditionally is a complex and manual process. Automation removed the need for tracking spreadsheets and provided clean data access to all stakeholders. Overall, the company reduced the time it takes to clean and monitor data by 20%.

Orchestration: Risk-based data review will be a central pillar in clinical data activity. A workbench can help a data manager ensure that data reaches the right stakeholders at the right time, assign and prioritize tasks to review data, and monitor progress all in one place.

Another top 20 biopharmaceutical company, in its shift toward RBDM, is using EDC and a clinical database to clean and visualize patient data. Going from individualized bespoke tools to a simplified, orchestrated approach has helped the company achieve first patient activated (FPA) to go-live in 9 weeks on average, and 80% standardization across studies. This level of data orchestration frees time for data managers’ other tasks and supports faster database lock.

A workbench functions as the central point of access across teams, allowing cross-functional collaboration on real-time data. For example, unexpected lab results for a patient submitted with no AEs can be flagged by a medical reviewer, making observations directly on the data and assigning it for action to a data manager by querying the site to confirm whether there are any reported AEs.

With the growing acceptance, trust, and scaling of agentic AI capabilities, the data manager’s orchestration responsibilities will expand from managing cross-functional data reviews to the more strategic role of data surveillance. As AI advances, regulatory bodies continue to provide guidelines for expectations, oversight, and good machine learning practice (GMLP) principles. The US FDA’s Guiding Principles of Good AI Practice in Drug Development, for example, provides 10 principles focused on human-centric designs, risk-based approaches, data governance, lifecycle management, and more. To successfully implement agentic AI for data management, understanding regulatory guidance and considerations are key to remaining compliant.

Establishing RBDM Strategy for Speed

Clinical data teams that prioritize RBDM and establish positive traction applying its principles to risk-based prioritized key data can drive faster and more compliant trials. There is a significant opportunity for improvement, yet on average, only 57% of a company’s clinical trials implement risk-based strategies. The key is a focused approach to RBDM, with clinical data aligning to RBQM programs in the future.

A clinical data workbench can streamline RBDM execution by centralizing data, understanding what it means, and automating processes. Data managers can then become data stewards for the collection, quality, and delivery of clinical data. This advances data managers’ roles from reacting and processing data toward focusing on proactive risk-based execution.

To automate clinical execution and eliminate the manual reconciliation of data, establish a connection for the workbench with other core systems that support clinical trials, including clinical trial management systems (CTMS), randomization and trial supply management (RTSM), and patient safety systems. Compliance with ICH E6(R3) computerized system expectations and FDA 21 CFR Part 11 for electronic signatures and records present additional considerations. Compliant systems that enable end-to-end processes and real-time data flow empower data managers to optimize their time and increase the potential value of their analysis.