$500,000 Lost a Day: The Economics of Clinical Trial Execution Is Pharma's Expensive Secret

The pharmaceutical industry is under enormous pressure to move clinical trials forward faster, and for good reason: development timelines are long, capital is expensive, and patients are waiting. In that environment, a lost day is never just a lost day; it comes with tremendous cost.

In 2024, Tufts Center for the Study of Drug Development (CSDD) helped quantify the scale, estimating that delayed market entry is worth approximately $500,000 in unrealized prescription drug or biologic sales per day, on average.

In some therapeutic areas, including cardiovascular disease, hematology, immunology, infectious disease, and oncology, the figure is even higher. Tufts has also estimated the direct cost of conducting a clinical trial at approximately $40,000 per day across phases, with phase 3 trials costing nearly $56,000 per day.

That cost eventually lands somewhere. It is borne first by sponsors trying to fund development and manage risk. It is felt by sites asked to do more with limited resources.

And, ultimately, it affects patients, who may wait longer for access to therapies they urgently need while also living in a healthcare system where the cost of development contributes to the cost of care. For patients with cancer, rare disease, neurodegenerative disease, or another serious condition, delayed development is not an abstract financial concept. It can mean more time without an option that could change the course of their disease.

If all delays were driven by scientific uncertainty, that would be one conversation. Drug development is difficult, and many therapies fail for legitimate reasons related to safety, efficacy, dose, biology, or patient selection.

Unfortunately, many delays are driven by poor execution processes the industry has learned to tolerate, especially at the point of care, where critical activities such as sample collection, processing, and documentation do not always fit cleanly into the systems sponsors use to monitor the study. Biological sample workflows are a good example because samples are often central to eligibility decisions, pharmacokinetics, safety monitoring, biomarker analysis, dose escalation, and go/no-go decisions.

They are also highly specific, requiring detailed instructions for collection timing, tube type, processing, storage, and shipment across multiple visits. To manage that complexity, sites often translate dense protocol requirements into handmade working guides, essentially manual checklists.

Those guides are practical but fragile: they depend on instructions being interpreted correctly, kept current, and followed at the right moment. Across a multi-site trial, that creates major inconsistencies in how the same protocol gets executed.

The same mismatch shows up in documentation. Most sponsors rely on electronic data capture (EDC) to review clinical trial data in a structured format, but EDC is rarely where the information is first recorded during the visit.

More often than not, critical information is captured first on paper (or the side of a sample tube, sticky note, paper towel, or even a medical glove) then transcribed into EDC later so the sponsor can see it. That is not a real-time process.

One Tufts CSDD industry survey found that it takes more than eight days, on average, from a patient visit to when data are entered into EDC, a lag that remains familiar in sponsor trials today.¹ That means sponsors may not see what happened until well after the patient has left the building. And every time information has to be re-recorded, there is another chance for an illegible note, a missing detail, a typo, or a timepoint entered incorrectly.

By the time an error surfaces through source data verification, it may be days, weeks, or longer after the visit. The sample may no longer be usable, the patient may need to return, and the sponsor may be left determining whether the data can still support the decision it was meant to inform.

Multiplied across sites, countries, and lab partners, what looks like a local workflow issue can become a material trial risk. An even more recent Tufts CSDD report gives useful language to this broader problem, describing it as the "execution translation gap."²

According to the report, sponsors and CROs are often able to identify trial issues, and the industry has invested heavily in dashboards, data feeds, monitoring tools, and analytics to support that detection. The gap is that awareness still does not reliably become coordinated action quickly enough to make a difference.

But that only accounts for the issues that surface. Because downstream tools can only identify what has been captured reliably enough to detect, the true scale of execution risk is likely significantly higher than the data suggest.

Even as a floor, the numbers in the report should make the industry uncomfortable. Tufts notes that protocol deviations per pivotal trial have increased substantially over the past five years, substantial protocol amendments now take an average of 260 days to fully implement, and phase 2 and 3 trials still require an average of 60 days to reach database lock.

Tufts also describes a scenario in which a 90-day delay in a phase 3 trial can add more than $5 million in direct costs to a single trial. Those figures raise the obvious question: if the economics are this clear, and if execution problems are this familiar, why has the model been so difficult to change?

Part of the answer is structural. Sponsors are under intense pressure to move faster and cheaper, manage vendors, maintain inspection readiness, and deliver clean data under increasingly complex study demands.

Under those conditions, change can look risky even when the current process is clearly creating problems. Sponsors are also deeply dependent on sites because sites have what sponsors need most: access to patients.

No sponsor wants to ask an already stretched site team to adopt yet another process, especially when it has to compete with existing workflows, other active studies, and day-to-day patient care. This is what makes the industry's current investment priorities so paradoxical.

Biopharma companies and investors are spending heavily on AI-enabled discovery, predictive modeling, and other tools designed to improve the probability that the right candidates enter the clinic. But the candidate that enters the clinic still has to be proven in a clinical trial, and that proof can depend on data from a relatively small number of patients.

AI cannot fix a weak data foundation. If critical trial information is captured on paper, entered after the fact, and reviewed only after the opportunity to intervene has passed, the industry may be moving data faster without making the trial itself more reliable.

The industry also needs to be honest about the incentives built into the current model. Sites are often paid based on enrollment and completed visits, while CROs and vendors are frequently compensated for the work required to manage, monitor, and resolve trial issues over time.

That is a structural problem, not a matter of individual bad faith. Most people in clinical research are doing their best under difficult conditions.

But when the system rewards speed, volume, and downstream issue management more clearly than it rewards quality at the source, it creates the wrong incentive structure. The added risk is that routine data cleanup can create cover for more serious problems.

Intentional misconduct is expected to be less common than ordinary execution error, but recent high-profile headlines make clear it is not hypothetical.³ When questionable data takes too long to detect, or is undetectable in the first place, sponsors may not know whether the data can be trusted until the trial has already absorbed the risk.

Modernizing clinical trial execution is therefore not simply about moving faster. It is about building quality into the moment the work is performed, giving sponsors timely visibility into what is happening at the point of care, and giving site teams tools that support the visit instead of adding more burden after the fact.

Patients are giving their time, their trust, and often their biological samples to help generate reliable answers. For an industry already under pressure to move faster, the standard has to be better execution, not faster cleanup.

Disclosures

Dr. Graham is the Chairman and Co-Founder of TruTechnologies.

References

1. eClinical Data Volume and Diversity Pose Increasing Challenges and Delays. Tufts Center for the Study of Drug Development.
2. Recognizing and Addressing the Execution Translation Gap in Clinical Trials. Applied Clinical Trials Online.
3. Alzheimer's drug developers accuse clinical trial sites of faking data. Science. AAAS.