Recognizing and Addressing the Execution Translation Gap in Clinical Trials

Drug development has long recognized two well-established translational blocks, each rooted in the generation and application of scientific knowledge. The first arises in the transition from basic science to clinical research—the challenge of applying discovery and preclinical insights into clinical research activity. The second occurs in integrating clinical research findings into routine clinical practice, where issues of adoption, reimbursement, and physician behavior limit the diffusion of therapeutic innovation. These two translational barriers have dominated both academic discourse and policy intervention.

There is another translational block, however, that is arguably more pervasive and operationally consequential, yet it remains under-recognized: i.e., the execution translation gap. This gap reflects the systemic inability to convert well-identified problems into coordinated, timely, and effective action. In a domain as complex and interdependent as clinical trials, this gap manifests in the failure to mobilize cross-functional teams, align incentives, adapt processes, and implement corrective strategies and tactics. The execution translation gap is not a deficit of knowledge; rather, it is a failure to execute clinical activity efficiently and effectively. Its consequences are measurable, material, and growing.

Over the past decade, empirical research—in particular, studies conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD)—have started to illuminate this execution translation gap. Study findings reveal a pattern of persistent inefficiencies, recurring errors, and delayed responses that collectively harm clinical trial performance, cost, and quality. This article characterizes the execution translation gap, quantifies its economic and operational impact, and outlines strategies and practices to help mitigate and ultimately prevent it.

Characterizing the execution translation gap

At its core, the execution translation gap reflects a breakdown in organizational capability. Sponsors and contract research organizations (CROs) are generally adept at identifying problems. The proliferation of data dashboards, real-time analytics, and data monitoring tools has significantly enhanced signal detection. However, the conversion of these signals into decisive, coordinated action remains inconsistent.

This gap is best understood through the lens of organizational alignment and operational readiness. Clinical trials are inherently cross-functional, requiring tight coordination among clinical operations, data management, regulatory affairs, biostatistics, pharmacovigilance, and, often, external intermediary service providers. Each function operates within its own set of priorities, metrics, and incentives. When a problem or pattern is identified—such as an increase in protocol deviations or lagging site enrollment—effective response requires synchronized action across these functions and external providers. In practice however, such synchronization is often slow, fragmented, or incomplete due to, among other causes, the existence of functional silos.

Moreover, legacy processes and rigid standard operating procedures (SOPs) may inhibit agile adaptive response. Clinical development organizations are highly regulated, and compliance imperatives tend to favor consistency over agility. While this ensures quality and a straightforward audit trail, it can also create inertia, delaying the implementation of necessary changes. The result is a structural mismatch: dynamic problems confronted by static and slow processes.

Manifestations of the gap

The execution translation gap is not an abstract construct; it is observable in multiple facets of clinical trial conduct. Tufts CSDD research is only beginning to characterize the many areas where this gap is particularly prevalent.But several recurring patterns illustrate its scope and persistence.

Protocol deviations represent one of the most visible examples. Despite being identified, documented, and ostensibly resolved, similar deviations frequently recur across sites and studies. Over the past five years, the mean number of protocol deviations per pivotal trial has increased by 56%, rising from an average of 189 per phase III clinical trial in 2020 to 296 in 2025. This trend suggests that root causes are not being effectively addressed. Instead, corrective actions are localized and transient, failing to achieve systemic learning.

Protocol amendments provide another example. Substantial amendments—those involving changes to eligibility criteria, endpoints, or procedures—now take an average of 260 days to fully implement, from initial identification to final approval and participant re-consent. This duration represents a 154% increase over that measured in 2010. The delay reflects not only regulatory and ethical review timelines, but also internal coordination challenges, document version control issues, and site-level implementation variability. The inability to rapidly operationalize necessary changes prolongs clinical trial durations, introduces inefficiencies and increases cost.

Enrollment achievement also underscores the gap. Approximately half of all investigative sites in any global phase III trial either fail to enroll any patients or under-enroll relative to targets. This distribution has remained remarkably stable for over two decades, indicating systemic inefficiencies in site selection and activation. Despite advances in feasibility assessment and data-driven site identification, execution at the site level continues to fall short.

Study start-up timelines have increased. The average period from protocol approval to first patient first visit (FPFV) has expanded by 30–45% since 2015. This timeline involves a complex sequence of activities, including site qualification and selection, contract and budget negotiation, ethical review, site initiation, site engagement and participant screening—all of which require coordination among multiple stakeholders. Delays in each component cascade through the duration of study start-up.

Database lock timelines also reflect the execution translation gap. Despite significant investment in electronic data capture (EDC) systems and real-time data monitoring, phase II and III trials still require an average of 60 days to achieve database lock. A contributing factor is the growing number of intermediaries—e.g., data vendors, central labs, imaging providers—each introducing additional data streams and reconciliation requirements. Coordination across these entities remains a bottleneck.

Perhaps most striking is the disconnect between stated adoption of best practices and actual behavior. In a 2023 Tufts CSDD analysis, a high percentage of sponsors and CROs reported implementing risk-based quality management (RBQM) principles. Yet many study monitors continued to perform 100% source data verification (SDV) and review (SDR), practices that RBQM is explicitly designed to reduce. This inconsistency highlights the execution translation gap in its purest form: strategic intent without operational follow-through.

Quantifying the impact

The cumulative effect of these inefficiencies is substantial. At the macro level, clinical development success rates remain low. More than 90% of investigational drugs and biologics fail to progress from investigational new drug (IND) filing to regulatory approval. While scientific factors—efficacy and safety—account for the majority of failures, operational issues contribute to nearly one-third. These include inadequate participant enrollment, poor data quality and research integrity, and protocol non-compliance—each directly linked to execution.

From an economic perspective, the implications are significant. Tufts CSDD estimates that the average direct cost of conducting a phase III clinical trial is currently approximately $56,000 per day. This figure excludes indirect costs such as internal staffing and infrastructure costs. Delays attributable to execution inefficiencies—whether in start-up, amendment implementation, or database lock—translate directly into millions of dollars in additional expenditure.

Consider a conservative scenario: a 90-day delay in a clinical trial due to unplanned protocol amendments, slow replacement of clinical supplies, and longer-than-expected time to reach database lock. At $56,000 per day, this equates to over $5 million in incremental direct costs for a single trial. Multiplied across a portfolio of trials, the financial impact becomes material at the enterprise level. Moreover, delays defer potential revenue from successful products, amplifying the opportunity cost.

Beyond cost, the execution translation gap affects data integrity and regulatory confidence. Persistent deviations, inconsistent data quality, and delayed issue resolution increase the risk of regulatory queries, inspection findings, and even failure to achieve regulatory approval. In an environment where regulators are placing greater emphasis on data reliability and patient safety, executional shortcomings carry high strategic risk.

Addressing the execution translation gap

Achieving executional excellence requires team readiness to respond efficiently and effectively to critical operating risks and problems. Sponsors, CROs, and sites are using more advanced dashboards and tools to identify issues. But identification alone falls short because it is not translating into an effective executional response.

Recognizing the underlying causes of the execution translation gap is an essential first step. There are many interrelated root causes including:

1. Fragmented accountability: Responsibility for execution is often diffused across functions and external partners, leading to ambiguity and delayed decision-making.
2. Misaligned incentives: Performance metrics may prioritize functional efficiency over cross-functional outcomes, discouraging collaboration.
3. Process rigidity: SOPs and governance structures may lack flexibility, impeding rapid response to emerging issues.
4. Insufficient training: Staff and site personnel may not be adequately trained to perform in an agile manner or to implement new processes or technologies.
5. Technology underutilization: Advanced analytics and AI tools are often deployed for detection rather than action, limiting their impact at scale.
6. Complex protocol designs: Increasing numbers of endpoints and procedures per protocol add operational burden, amplifying the risk of deviations and delays.

Applying principles from recent regulatory guidelines to reorient teams is a critical next step toward helping to connect signal detection of a risk or problem with optimized operational execution. This can be achieved by leveraging critical thinking, fit-for-purpose proportional risk management, and AI- and technology-enabled approaches to simplify protocol design and execution, and reduce site and participant burden:

• ICH E6 (R3): Reinforces the integration of risk-based quality management principles, emphasizing proactive risk identification, continuous monitoring, and documented quality systems. It clarifies sponsor oversight responsibilities, particularly in outsourced environments.
• ICH E8 (R1): Shifts the focus upstream, requiring that protocol design decisions be tied explicitly to critical-to-quality (CtQ) factors. This encourages simplification by eliminating non-essential elements that add complexity without scientific value.
• FDA’s December 2024 draft guidance on protocol deviations: Introduces a more structured approach to deviation classification, documentation, and reporting. This facilitates root cause analysis and systemic learning.

Collectively, these guidelines promote a more holistic approach: aligning design, execution, and oversight around critical risks and outcomes.

The next step in addressing the execution translation gap requires a shift from reactive problem mitigation to proactive execution management. Several strategic levers can drive this transformation.

1. Define Execution Risk Indicators (ERIs) upfront

Analogous to key risk indicators in RBQM, ERIs focus specifically on execution. Examples include time-to-resolve data quality issue, time-to-implement amendments, deviation recurrence rates, and site activation timelines. Establishing thresholds enables early intervention and setting targets drives focus.

2. Align incentives and accountability

Performance metrics and unit costs should be redesigned to reward cross-functional outcomes rather than siloed inefficiency. Outcome-driven, clear ownership of execution metrics is critical.

3. Enhance training and capability building

Continuous training programs should focus not only on compliance, but also on problem-solving, critical thinking, AI-coworking and flexible execution.

4. Leverage technology for action, not just insight

Industry needs to think beyond the systems of records and leverage systems of reasoning and execution. AI and analytics should be embedded into decision workflows, enabling agentic orchestrations, automated triggers and AI-agent guided action paths to identified risks.

5. Foster agile operating models

Organizations should adopt more flexible, hybrid governance structures across AI-Human workflows, allowing rapid decision-making and iterative improvement.

Concluding thoughts

The execution translation gap represents a critical, yet under-recognized barrier in clinical trial performance and quality. Unlike traditional translational blocks rooted in scientific uncertainty, this gap arises from organizational and operational shortcomings. Its manifestations—persistent deviations, delayed amendments, underperforming sites, and prolonged timelines—are measurable and consequential.

Clinical trials are becoming more complex and resource intensive. As a result, the cost of poor execution will undoubtedly increase. Addressing this gap is, therefore, not optional, but rather a strategic imperative. By aligning incentives, simplifying processes, leveraging technology, and embracing regulatory guidance, sponsors and CROs can move toward executional excellence. In doing so, they will not only improve efficiency and reduce cost, but will also enhance the reliability of clinical evidence and, most importantly, accelerate the delivery of new therapies to waiting patients.

Ken Getz and Kenneth Kaitin, Tufts Center for the Study of Drug Development, Tufts University School of Medicine

Sources

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