Data-Based Decision-Making

“Most biopharma companies are data-rich but insightful-decision-poor,” says Shreya Modi, a Principal in ADL’s Healthcare & Life Sciences practice. She advocates for always getting an external view on the process and says the vast array of data collected by companies about clinical trials should be used more consistently to support decision-making. “That way, teams don’t rely on assumptions based on anecdotal knowledge. There is a wealth of data that can be leveraged. The key is to clearly identify and align on the problem to be solved and then use the data meaningfully to address it,” she says.

Competition is a useful example, as it is often viewed as a threat to patient recruitment. In reality, the situation is more nuanced. “From our work in geolocation-based competitive-impact analysis, we’ve seen cases where colocated ‘competing’ trials actually outperform expectations,” says Modi. “In one uncommon disease trial-acceleration effort, sites running four to five similar studies delivered the strongest recruitment results.” The reason is that density drives momentum. These sites benefit from established referral pathways, stronger patient awareness, and informal collaboration across programs. “Instead of fragmenting the pool, proximity can activate it,” she adds.

Modi also emphasizes the importance of predictive intelligence over all-too-common lagging indicators. High-quality data analysis helps clinical trial sponsors better predict potentially delaying events so they can correct them early.

“We aim to pick up signals as early as possible and act decisively. If it turns out to be a real issue, we’re already ahead of it. If not, the downside is minimal — just a few days of effort or dollars spent,” Enejo says. “Too often, the industry waits for signals to become obvious before acting. We work with companies to shift that mindset toward earlier, more proactive intervention.”

Governance

Keeping track of competing trials, evolving regulations, and studies taking place across multiple sites — often in different countries — is difficult. Compounding the challenge, different teams are frequently responsible for different parts of the trial process, and communication between them is not always effective. Ultimately, clinical trials need leadership that can move at the speed of change.

“All these leadership decisions are based on changes within the landscape, and they need to happen fast because every day, every hour, every week changes the landscape when you are recruiting patients for your trial,” says Ron Kwok, Partner in ADL’s Healthcare & Life Sciences practice.

For example, a new trial may be published or a new drug approved that changes the standard of care six months into your trial — while patient recruitment is still underway. That creates an ethical dilemma: patients in the control group should receive the best available treatment, even if that treatment is no longer the one being used in your trial.

The solution to challenges like these — and to the broader need to accelerate trials — is to appoint a senior-level person to oversee the entire clinical trial process. This structure also helps strengthen alignment between R&D and the company’s broader strategic goals. To succeed in the role, that person must have both the bandwidth to manage the complexity and the authority to drive value across the organization.

“Many times, there is no one person saying, ‘OK, how do we make this a much more efficient process so that instead of taking three years, we take two and a half years?’” says Emmanuel Aisabokhae, Principal in ADL’s Healthcare & Life Science practice. “For example, the clinical scientist’s job is to design the protocol, and their focus is probably on scientific robustness. They’re not necessarily thinking about designing a protocol that makes recruiting as fast as possible.”