Making Unaccounted Costs Accountable in Clinical Research

Clinical trials operate under strict timelines, regulatory requirements, and complex coordination between sponsors, CROs, sites, and vendors. When delays occur or issues arise, project teams often resolve problems by working longer hours rather than by adjusting timelines, budgets, or processes.

In many organizations, a standard working day is approximately eight hours. However, Clinical Research Associates, Project Managers, and other clinical operations staff may regularly work ten or more hours per day, especially during critical study phases.

If only eight hours are recorded in time sheets while ten hours are actually worked, two hours per day become unaccounted operational effort.

Individually, this may appear minor. Over the duration of a clinical trial, the financial impact can be substantial.

Financial Example: Unaccounted Hours Over a Project Duration

Hidden, Absorbed, and Unaccounted Costs in Clinical Trials

Clinical trials are widely recognized as expensive and complex projects. Budget discussions usually focus on visible cost elements such as labour fees, investigator grants, site payments, vendor contracts, other passthorough. These costs are clearly documented in study budgets and contracts and are typically tracked through project accounting systems.

However, the real operational cost of clinical trials often extends beyond these visible budget lines. During study execution, additional effort frequently arises from inefficient processes, fragmented systems, and operational challenges that require extra coordination by project teams.

Some of this work eventually appears in project effort reports. Some of it is absorbed by organizations when project budgets are exceeded. And some effort may never be formally recorded at all.

To illustrate this situation, it can be useful to distinguish between four different categories of costs in clinical trial operations: visible costs, hidden costs, absorbed costs, and unaccounted costs.

What Are AI Agents and How Can They Help Optimize Clinical Trial Operations

Removing duplication in clinical trial operations could significantly reduce costs and increase research capacity. One emerging technology that may help enable this transformation is the concept of AI agents. But what exactly are AI agents, and how could they improve clinical trial operations?

What are AI agents?

An AI agent is a software system designed to perform tasks autonomously based on defined goals, available data, and operational rules.

Traditional software typically executes predefined commands. AI agents, in contrast, can interpret information, analyze context, and trigger actions within complex workflows.

In practical terms, AI agents can:

• interpret structured information

• coordinate operational processes

• trigger workflows across systems

• monitor data and identify anomalies

• generate reports or documentation

Rather than acting only as databases or static tools, AI agents can function as digital operators that help coordinate processes and information flows.

Why clinical trial operations are suitable for AI agents

Could Clinical Trial Costs Be Reduced by Half?

Could Halving Clinical Trial Costs Double the Benefits of Clinical Research?

Clinical trials are essential for developing new therapies, but they are also widely known to be expensive. Conducting a single clinical study often requires large budgets, complex coordination, and significant operational infrastructure.

But this raises an important question: How much of the cost of clinical trials is driven by scientific requirements—and how much is driven by operational complexity?

Clinical trials must support patient care, medical procedures, and the evaluation of investigational treatments. These elements are essential and cannot be eliminated. However, a substantial portion of clinical trial budgets is also devoted to coordinating processes, documenting activities, reconciling information across systems, and managing regulatory workflows.

In this sense, clinical trials are not only scientific investigations. They are also large operational systems.

Understanding how these systems are organized may reveal opportunities to improve efficiency without compromising scientific quality.