Understanding DPIAs in Clinical Research

Under the EU General Data Protection Regulation (GDPR), a Data Protection Impact Assessment (DPIA) is required when data processing is likely to result in a high risk to the rights and freedoms of individuals. This requirement is especially relevant to clinical trials, relying on sensitive health-related data, genetic information, and where real-time biometric inputs from wearable devices are often processed.

A DPIA is a structured and proactive risk assessment conducted before a project begins. Its goal is to identify the nature of the processing, evaluate risks to individuals, and determine how those risks can be mitigated through technical and organizational measures. DPIAs are essential not only to meet GDPR obligations, but also to demonstrate ethical responsibility, ensure regulatory readiness, and build trust with participants and partners.

This leads many sponsors to a practical and strategic question: should a DPIA be conducted for every clinical trial, or can a single DPIA at corporate level cover the processing activities of all trials?

DPIA Per Trial vs. Corporate-Level DPIA

The GDPR does not explicitly state that a DPIA must be done per trial. It allows organizations to avoid repetitive DPIAs for processing activities that are very similar, provided that the risks are the same and already covered. In theory, this opens the door to conducting one DPIA at the corporate level to cover all clinical trial-related data processing. However, in practice, this approach is difficult to justify.

Clinical trials vary widely in protocol design, countries involved, data collected, types of participants, technology platforms used, and third-party partners engaged. These differences significantly affect the risk profile. A centralized DPIA may be appropriate for standardized, low-risk, repetitive processes, but it will rarely be adequate for the complexity and variability of modern clinical research.

While some organizations attempt to build a baseline DPIA framework and update it per trial, it is critical to understand when such an approach is appropriate—and when a separate DPIA is necessary.

Can a Baseline DPIA Be Adapted?

Some sponsors explore the idea of conducting a baseline DPIA for common processing activities and adapting it per protocol. This approach may be defensible when the foundational processing risks are well-understood, the data lifecycle is stable across studies, and the organization commits to reviewing and supplementing the baseline DPIA for each trial.

This means treating the baseline DPIA as a living framework, with clear procedures in place to reassess risks whenever new data categories, jurisdictions, technologies, or third-party vendors are introduced. When applied rigorously, this method can reduce administrative duplication and help organizations maintain a consistent risk analysis model across studies.

That said, this approach has real limitations. In practice, clinical trials are rarely uniform. Protocol-specific variables, including study design, population characteristics, data collection tools, regulatory environments, and technology use, often introduce new or elevated risks that cannot be fully anticipated in a generic DPIA. The baseline model is only as good as the organization’s ability to detect these shifts early and document a robust reassessment.

From a risk management perspective, relying too heavily on a generic DPIA, even if adapted, can lead to blind spots. It may also fall short of what regulators or ethics committees expect, particularly in multi-country trials or those involving emerging technologies like AI or decentralized data collection.

In short, while a modular or baseline DPIA can serve as a starting point, it should never be treated as a substitute for protocol-level analysis. Trial-specific DPIAs remain the more defensible, and often necessary, approach when dealing with high-risk, high-variability processing environments like clinical research.

When Is a DPIA Required in Clinical Trials?

Under the GDPR, a DPIA must be conducted when data processing is likely to result in a high risk to the rights and freedoms of natural persons. The European Data Protection Board (EDPB), which continues the work of the former Article 29 Working Party, outlines key criteria to assess whether this threshold is met. These include the use of special category data (such as health, genetic, or biometric data), reliance on innovative or emerging technologies (like AI or remote monitoring tools), large-scale processing, systematic monitoring of individuals, and the involvement of third parties or cross-border data transfers. In the context of clinical trials, several of these risk factors are often present simultaneously, especially in multicenter or international studies involving digital platforms, wearable devices, or outsourced data processing. Therefore, sponsors should not rely on any single criterion, such as scale, but instead assess the overall risk profile of each trial, taking into account its data sensitivity, complexity, and geographic scope.

Not All Trials Are High Risk—But Most Are

It is a common misconception that every clinical trial automatically triggers a DPIA requirement. While many trials do involve high-risk processing due to the nature of the data and the complexity of operations, this is not universally the case. For instance, a small, monocentric trial that uses minimal technology, collects only limited pseudonymized sensitive data, and does not involve international transfers or third-party processing may not meet the high-risk threshold. However, such situations are the exception rather than the rule. Given the increasing digitization of clinical research and the routine use of special category data, most modern trials do fall within the scope of Article 35 GDPR. As a result, sponsors should assume that a DPIA is required and clearly document their justification if they decide otherwise. Recording the rationale for not conducting a DPIA in a clinical trial is not only essential for demonstrating accountability, but also for managing potential scrutiny from data protection authorities or ethics committees.

What Do Regulators and Ethics Committees Expect?

Although the GDPR provides room for flexibility, supervisory authorities expect DPIAs to reflect the real-world complexity of individual trials. They are looking for DPIAs that show a clear understanding of the specific processing activities involved in each trial, consideration of the trial’s geographic, technological, and legal context, and a documented and reasoned assessment of the specific risks and mitigations implemented.

DPIAs Are Not One-Off Tasks

A DPIA is not a static document. As a clinical trial evolves; through protocol amendments, country expansions, the addition of new sites, or changes to the technology stack; the DPIA must be updated to reflect the new risk landscape.

For instance, if a trial that initially used paper-based consent introduces eConsent or remote patient monitoring halfway through, the DPIA must be revisited. Similarly, adding a new data processor or initiating secondary data use for future research or AI modeling would also require reassessment.

This dynamic nature of DPIAs reflects the GDPR’s emphasis on data protection by design and by default throughout the lifecycle of the processing.

Conclusion: Defining a DPIA Strategy for Clinical Trials

DPIAs are not simply regulatory checkboxes; they are practical tools for ensuring ethical, secure, and transparent data handling in clinical research. They provide sponsors with a framework for making informed decisions, avoiding preventable risks, and documenting accountability.

While it may be tempting to rely on a single corporate DPIA, especially in large organizations with many concurrent trials, this approach carries significant limitations. In most cases, the safer and more compliant path is to conduct a DPIA for each clinical trial, considering its unique characteristics and geographic scope.