Less is More in Clinical Data: The Road to Simpler and Better Trials

May 20, 2025 | Drew Garty

2025 is a dynamic time for clinical trials. In December 2024, Deloitte surveyed global life science executives and 75% said they were optimistic about 2025, driven by strong growth expectations, despite uncertainties in the industry.

The possibilities to advance better science in clinical data are expanding thanks to advanced technology and the rise of 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 created more complexity for the larger study team and ecosystem. The surge in new tech offerings requires us to take a step back and not only simplify trials for sites and patients, but also help study teams standardize with the right tools and processes.

Ibrahim Kamstrup-Akkaoui, VP 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 that we can learn even more from them for future initiatives.”

A pragmatic approach to clinical trials will help us to advance better science together, by focusing on the data that really matters, and minimizing surplus effort on processing redundant data. This is particularly important for data managers, who are increasingly stretched for time as their roles expand.

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 stand to simplify everyday experiences and reduce effort for sites, patients, sponsors, and CROs significantly.

To reach this goal sooner, leading sponsors are evaluating which practices they want to start doing, and also what they hope to stop:

Start…

…Consulting sites early when introducing new technologies

Sites now navigate hybrid data flows and collect data from diverse sources. When sponsors introduce new technology to manage increased data volume, it 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?’”

Stop…

…Waiting for AI to reduce workload

Many sponsors are prioritizing value-driven solutions and investing in technology that reduces cost and increases quality. AI is a great illustration of the ‘art of possible’, and it’s important 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 cost before we change processes.

To navigate this, we can start simplifying the heavy lifts like queries and form builds now, while also 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, if we can shave two minutes off every single query, just by making it a one-click experience, then that easier user 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 save millions of dollars per year by automating nearly all queries.

Standardizing our 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 really trying to avoid is using the same process with just a different tool.”

How can others put initiatives like this into practice?

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 we’re driving toward a one-week study build. But that time saving isn’t only about efficiency, it also gives study and scientific teams more time earlier 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.

Stop…

…Viewing statistics as a back end process

Studies are often started without knowing what data will be critical to statistics. Considering that clinical research is a statistical endeavor, it’s surprising that it sits at the back end. We need to shift mindsets and include statistics earlier in data management or even better, at trial design. 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 towards this long-term goal.

Realizing more value with less waste

In the pursuit of streamlined and impactful clinical trials, the principle of ‘less is more’ extends beyond mere efficiency to maximizing value.

Actionable strategies for clinical data leaders to optimize their approach include:

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…Taking the leap with 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 a way of 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’.

Stop…

…Collecting data that doesn’t get used

It’s not uncommon for stats teams to use only 40% of 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.

Turning vision into impact

Innovation in the life science industry almost always involves adding layers of complexity. Historical solutions are now our problem; it’s time we start peeling back the layers, collecting less data, streamlining technologies used, and focusing our efforts on the initiatives that drive the most value.

Movements like endpoint-driven design and AI won’t happen overnight. But, to advance better science, now is the time to start bringing pragmatic elements where possible, simplifying and standardizing clinical data management, and setting ourselves up for a future where we recognize that less is more.

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