What Is Clinical Trials Day? Why Technology Helps Research Rise

By John Oncea, Chief Editor, Clinical Tech Leader

In 1747, a ship's surgeon named James Lind selected 12 sailors with similar scurvy symptoms, kept them in the same quarters on the same diet, and assigned two men each to six different remedies. According to the James Lind Library, the pair that were given oranges and lemons improved fastest; one was fit for duty within six days. Lind’s deeper contribution was methodological: he showed that anecdotes aren’t enough and that treatments must be compared systematically rather than accepted on authority alone.

That principle is why clinical trials still matter and why the systems that run them must keep evolving. Clinical Trials Day, observed each year on May 20, recognizes the legacy of evidence-based medicine and the modern workforce that delivers it, increasingly with help from research technology that can reduce site burden, improve data quality, and speed enrollment. For many clinical leaders, the day is also a prompt to ask what technology actually reduces coordinator burden and improves accrual today?

Clinical Trials Day is championed by the Association of Clinical Research Professionals (ACRP), and this year’s theme, Research Rising, lands at a moment of genuine urgency. To understand what that phrase means on the ground, Clinical Tech Leader spoke with Noelle Gaskill, ACRP Board of Trustees Member and Vice President/General Manager of TIME Network at Tempus, and Mark Vance, Head of Engineering and Product, Apps, at Tempus.

Why Workforce Constraints Make Trial Tech Non‑Optional

“We all fail, including the patients who want access to clinical research, if we don’t support the clinical research workforce,” Gaskill said. That’s not an abstraction. In oncology, where clinical trials often represent a key component of the patient’s care journey, sites without adequate staffing routinely go on and off enrollment. Eligible patients are missed, not because the science isn’t there, but because the people needed to screen, enroll, and manage them aren’t.

“Clinical Trials Day is a perfect opportunity to celebrate the patients, the sites, and the clinical research workforce,” Gaskill said. “As the workforce is the front line of getting patients on trial.”

ACRP CEO Susan Landis framed it as the organization’s advocacy mission: elevating and amplifying “professionals whose dedication advances patient care, drives scientific advances, and improves health outcomes across the globe.”

Technology Is Here, And The Workforce Isn’t Sure What To Do With It

A common theme throughout this year’s ACRP Annual Meeting was unmistakable: Artificial intelligence (AI) is no longer a future consideration for clinical research professionals. It is a present one, and the workforce is not uniformly ready, but ACRP is fitting in to support the gap.

“What I saw in many of the questions I personally fielded from the stage was a lot of hesitation, a lot of fear,” Gaskill said. The concerns were consistent: PHI, FDA compliance, and sponsor requirements. But she argued the fear is often misplaced. “There is a very safe space to be using everyday tools and AI in clinical research workflows; as they’re labor-intensive, take a lot of time, and AI can often solve for those inefficiencies.”

Vance pointed to integration complexity as a compounding factor. Sites must toggle between dozens of point solutions depending on which sponsor is running which study. “Anyone who can unify that experience at the site will see adoption,” he said. “Even the best tool might only impact five percent of a site’s clinical trial portfolio if it’s not solving for the full picture.”

Gaskill used decentralized trials to illustrate how that dynamic has already shifted. Early DCT conversations placed the burden on sites to adopt entire sponsor-selected technology suites per study. That model has largely inverted. “The sites are now saying: I have my platform. You, pharma, need to let me use it,” she said. “With AI, the same principle applies; we need to support sites at a portfolio level, not through individual point solutions per study.”

The Workforce Challenge Is Bigger Than Anyone Expected

In oncology, the pace of scientific progress has outrun any individual’s ability to keep up. New biomarkers, new targeted alterations, new commercial products, and new corresponding trial options are emerging on what feels like a monthly basis.

“The care and navigation of the oncology therapeutics and trials is now too complex for any one person’s mental dictionary,” Gaskill said. “Technology needs to help providers navigate a landscape that’s constantly changing and only deepening in its complexity.”

Vance was blunt about the trajectory. “We’ve always been bad at putting patients on trials as an industry, and it’s just going to get harder,” he said. “Precision medicine raises the requirements. You practically need a genomic counselor to understand the data to actually fit a patient to a study.”

The skills gap is real, but neither Gaskill nor Vance framed it as primarily generational. The greater need, they agreed, is teaching clinical research professionals to validate AI-assisted decisions at scale, reviewing the work of tools rather than doing that work manually. “You have a great tool, but how do you build confidence in it?” Gaskill said. “Sites have to be highly scrutinizing of the technology and where they’re using it.”

What’s Working: EHR‑To‑EDC + AI For Matching And Data Review

Both pointed to the same bottleneck when asked which technologies are having the greatest impact: the gap between electronic health records and electronic data capture.

“EHR to EDC should be what we’re all driving for,” Gaskill said. “We see sites stop enrolling patients because they have to manage their data. If you can auto-populate case report forms and have a coordinator reviewing rather than inputting, with AI flagging what to double-check, that should be the wave of the future.”

Vance described the underlying data problem in stark terms. “If you knew how your data was stored in the EHR, you would be depressed,” he said. “A patient’s cancer stage is not structured within the EHR. It’s locked in documents and unstructured text. Cleansing and structuring that data is by far the biggest problem, and that’s where generative AI is helping the most right now.”

The results are measurable. Sites using AI to surface patients from both structured and unstructured data are identifying eligible patients faster and enrolling them at meaningfully higher rates than sites relying on manual screening. On timelines, Vance offered a sobering benchmark: roughly 80 percent of studies don’t enroll on time. “Automating patient matching not only makes it faster, but it finds more patients,” he said. “We should not be falling behind accrual timelines if every patient who fits can be identified within that window.”

Gaskill captured the stakes plainly. “It’s truly no patient left behind,” she said. “In a manual workforce, you just can’t reach or look at them all. We’re enabling that.”

The Bigger Shift: Putting The Patient In The Driver’s Seat

When asked about the cultural change that matters most, Gaskill moved past tools entirely. “The patient doesn’t know what the protocol looks like,” she said. “They may not even be familiar with their disease at the level they need to be comfortable with research itself. If we can change that dynamic, from us driving clinical research to patients being a part of the process, perhaps even driving it, that’s the biggest shift.”

Looking five years out, Vance sees technology helping erase the line between standard care and research. “If we can identify patients within their point of care exactly when a research opportunity is available and remove the additional data burden through EHR to EDC, there’s an opportunity to bring those worlds much closer together,” he said.

Asked what James Lind would make of modern clinical research, Gaskill acknowledged the complexity he’d encounter first. “He’d say, ‘You guys created a lot of rules and redundancies, what are you doing?’” she said. “But I hope he’d also say, ‘Wow, it’s a lot more pervasive today. There’s an entire industry around clinical research now.’ And hopefully he’d say, “They’re actually introducing it along the patient journey.’ Patients have options. That’s the point.”

That is exactly the point Clinical Trials Day exists to make. From a citrus experiment aboard HMS Salisbury to AI-assisted trial matching, the goal has never changed: generate reliable evidence and get it to the patients who need it. The tools are different. The urgency is not.

What Tech Leaders Can Do Next

Tech leaders should begin by mapping where coordinators lose time today, whether through duplicate data entry, repeated query resolution, or time spent hunting for documents, and prioritize automations that reduce clicks rather than introduce additional steps. From there, they can advance EHR-to-EDC integration through tightly scoped pilots focused on narrow, measurable workflows such as demographics, labs, or vitals, before scaling to more complex data elements. At the same time, planning for unstructured data is essential; teams need to identify which trial-critical variables reside in clinical notes or pathology reports and define where human review remains necessary when AI is used for extraction.

In parallel, organizations should establish a clear AI validation model that defines who reviews outputs, what data is logged, how model drift is monitored, and how protected health information is secured across tools and vendors. Finally, platform evaluation should emphasize portfolio-wide coverage and interoperability, including single sign-on, API availability, and standards support, so that sites can operate within a unified workflow across sponsors rather than navigating fragmented systems.

Clinical Trials Day is celebrated annually on May 20. Learn more at clinicaltrialsday.org.