Key takeaways
Simplify with purpose: prioritize what adds value: Modern trials are overloaded with data and systems that often yield diminishing returns. To reduce burden on sites and patients, clinical teams must stop collecting unused data and focus on critical endpoints. Risk-based and endpoint-driven designs streamline operations, improve data quality, and reduce waste.
Modernize processes, not just tools: Adopting new technologies isn’t enough—processes must evolve too. Clinical leaders should focus on foundational change: automating repetitive tasks like query management, involving statisticians early in trial design, and shifting from patchwork systems to unified, connected data environments.
Engage sites early, design with them in mind: Sites juggle up to 20 systems per study. Sponsors must consult sites before introducing new tech and prioritize user-friendly solutions. Investing in site relationships and workflows creates trials that are not only faster but also more site- and patient-centric.
Despite uncertainties in the industry, global life sciences executives remain optimistic and have strong growth expectations. Yet, it is safe to say it’s a dynamic time for clinical trials.
The possibility of delivering better clinical data science in trials is expanding thanks to advanced technologies and the rise of artificial intelligence (AI). Much of this expansion has been propelled by the need to address the increasing complexity of clinical trials. Patchwork technology and processes have tried to solve singular issues, but those siloed efforts often increased the burden for the larger study team and ecosystem. The surge in new tech offerings requires a step back to simplify trials for sites and patients and help study teams standardize with the right tools and processes.
Ibrahim Kamstrup-Akkaoui, vice president for data systems innovation at Novo Nordisk, describes the changes he envisions: “I think something drastic really has to happen. We have new requirements for innovative trial designs, and data managers need to make sure that data are in good shape and facilitated in a way so we can learn even more from them for future initiatives.”
A pragmatic approach to clinical trials will help the industry focus on the data that matters, minimizing surplus effort on processing redundant data. This is particularly important for data managers, who are increasingly stretched for time as their roles expand.
To advance toward standardized trials with speed, there are key areas of opportunity where data leaders can stop legacy processes and start with a modern approach. On International Clinical Trials Day, here are recommendations to deliver better experiences, improve ways of working, and reduce waste in clinical trials.
Finding value in simplified experiences
Industry leaders have identified risk-based methodologies as the initiative they see most value in and most likely to succeed in the near term, with interest in real-world evidence and endpoint-driven design on the rise. Endpoint-driven design prioritizes data cleaning tasks by criticality, encouraging stats teams to interrogate whether endpoint data is missing or unlikely to be used earlier in the process. This enables risk-based data management.
These approaches will simplify everyday experiences and significantly reduce the effort of sites, patients, sponsors, and CROs. To reach this goal sooner, leading sponsors are evaluating which practices will drive the most value.
Stop: Waiting for AI to reduce workload
Many sponsors prioritize value-driven solutions and invest in technology that reduces cost and increases quality. AI is an excellent illustration of the ‘art of possible,’ and it’s essential that we prioritize and pilot suitable use cases. But AI won’t solve everything, and there are practical improvements to reduce workload today. We often have the technology long before we have the solutions, increasing costs before we change processes.
To navigate this, we can simplify the heavy lifts like queries and form builds while building a better foundation for AI. Smart automation is a use case that generates real value today, with rule-based automation speeding up data management. For example, shaving two minutes off every query by making it a one-click experience saves meaningful time (and cost) for sites. One-click queries are an example of a pragmatic solution that simplifies and standardizes work and can deliver millions in savings per year by automating nearly all queries.
Start: Consulting sites early when introducing new technologies
Sites now navigate hybrid data flows and collect data from diverse sources. Sponsors introducing new technology to manage increased data volume can inadvertently create hurdles for sites. By investing in site relationships as well as site technologies, sponsors can select the most site-centric solutions as identified by sites, not the sponsor.
“I’d like to think that by adopting the right technologies, we can run the right trials in the right way,” comments Kamstrup-Akkaoui. “Today, when we conduct a clinical trial, we give the sites and patients around 20 systems to deal with. I think we need to look inwards and say, ‘How can we change this to make life a bit easier for the users?’”
Standardizing ways of working
A connected data foundation is crucial for maximizing efficiency in clinical trials. Breaking down silos between functions and standardizing processes can mitigate risk and reduce costs significantly.
Bryan Kropp, AVP, head of clinical data management and standards at Merck, explains why the company prioritized modernizing and simplifying its clinical trial ecosystem. “As a result of a unified platform, it provided an opportunity for many different stakeholders to come together and really understand the increased connectivity. One of the things we’re trying to avoid is using the same process with just a different tool.”
To enable more streamlined processes, clinical data leaders can:
Stop: Viewing statistics as a back-end process
Studies are often started without knowing what data will be critical to statistics since it sits at the back end. Shifting the mindset and including statistics earlier in data management—or at trial design—will become a priority. The challenge is freeing up statisticians’ time to make this possible. Although there’s no silver bullet, having conversations and asking each other questions will help us move toward this long-term goal.
Start: Spending less time building studies and more time on the protocol design
We need to consider the role of all stakeholders in data flow to help identify opportunities to simplify and standardize across a study. For example, it is now possible to build a study in four weeks using a connected clinical data environment, and it's moving toward a one-week study build.
Time-savings isn’t only about efficiency. It gives study and scientific teams more time to pressure test the protocol design. By focusing on the highest areas of effort in study build and conduct, we can generate greater efficiencies upstream and downstream.
Realizing more value with less waste
In pursuing streamlined and impactful clinical trials, the principle of ‘less is more’ extends beyond efficiency to maximizing value. Actionable strategies for clinical data leaders to optimize their approach include:
Stop: Collecting data that doesn’t get used
It’s not uncommon for stats teams to use only 40% of the data collected in clinical trials. Every data point should have value and impact. By focusing on what can be done today—reviewing data collected against data used by statisticians at the end of the study to prove the endpoints—we can minimize resource waste. Data managers can then use insights from this exercise as input into the next trial to optimize study design.
Start: Adopting risk-based approaches to reduce study timelines
Regulators have long encouraged risk-based approaches, but the principle was never intended to stop at monitoring. It was meant to be about thinking and working, targeting what matters most to extract maximum value. In clinical data, this means focusing resources on the most important tasks to maximize their value potential. This is more measurable than solely looking at ‘efficiency gains.’
Turning vision into impact
Legacy solutions have added layers of complexity that are holding companies back from achieving efficiencies. It’s time to start peeling back the layers to collect less data, streamline the technologies used, and focus on initiatives that drive the highest value.
“Sometimes the most fundamental, operationally meaningful things aren’t flashy,” comments the global clinical operations leader at a top 10 biopharma company. “It’s our job as the data geeks to inform our leaders of why this thing that sounds boring is actually going to let us accelerate.”
Movements like endpoint-driven design and AI won’t happen overnight. But to advance data science, now is the time to start bringing pragmatic elements into trials where possible. This will help organizations simplify and standardize clinical data management, setting the industry up for a future where it recognizes that less is more.
Drew Garty, chief technology officer, Veeva Clinical Data
