Industry Perspectives on the Key Challenges in Trial Optimization

The holy trinity of trial optimization

At the core of trial optimization lies what the authors call the "holy trinity" of optimization and artificial intelligence: data, processing, and analytics. Data plays a pivotal role, with providers—whether commercial or public—aggregating and supplying access to a wide range of critical information. This can include data such as epidemiology, market access, standard of care, drug reimbursement, enrollment statistics, trial directories, and electronic medical records (EMR). In the past decade, the number of data providers has grown, and intensified in momentum more recently with the rapid advancement of AI technologies.

However, data alone is insufficient without the other components of the trinity. Processing power, the second element, is driven by companies like Nvidia,¹ which specialize in creating advanced computer systems tailored for AI-driven models. Yet, despite progress in data and processing, the third element—analytics—remains underdeveloped in the healthcare industry, as perceived by industry professionals. While numerous organizations aggregate data, few go beyond their proprietary datasets, limiting their ability to offer comprehensive, industry-wide solutions.

Data, data everywhere, but not an actionable insight

The phrase "Data, data everywhere, but not an actionable insight" is a deliberate play on the famous line "Water, water everywhere, nor any drop to drink" from Samuel Taylor Coleridge's The Rime of the Ancient Mariner. Just as the mariner is surrounded by undrinkable water, the healthcare industry is inundated with data that often lacks actionable value. This analogy highlights the growing challenge of having access to vast amounts of data without the ability to translate it into meaningful insights for decision-making.

In a survey conducted by Aurora Analytica, industry professionals were asked about the platforms they rely on for the design, planning, and execution of clinical trials.² Over 90% of respondents reported using a clinical trial directory or site aggregation platform, with Citeline³ being the most widely recognized.² Additionally, 65% use internal company tools, while 55% still rely on Excel for managing critical processes.² Fewer than 50% of respondents use more specialized platforms covering EMR, standard of care, and recruitment solutions within the clinical trial life cycle.² This raises the question: how can these datasets be leveraged in an actionable way?

Source: Aurora Analytica

Source: Aurora Analytica

Despite the variety of platforms in use, over 60% of respondents indicated they lack the necessary tools to effectively leverage data for trial design, planning, and execution, even though most companies use at least five platforms at different stages of the clinical trial life cycle.² When asked to rate their experiences with these platforms, individual responses ranged from mostly negative to overwhelmingly positive. Citeline, the most frequently used platform, scored an average of 4.5 out of 6 across respondents.² However, no single platform received an average score higher than this, highlighting the gap between user expectations and the solutions currently available in the industry.

Source: Aurora Analytica

Falling into a false economy

The costs associated with maintaining access to data platforms can vary dramatically, ranging from $10,000 to over $300,000 per platform annually, as shown in the Aurora Analytica Insight Survey.² Beyond these costs, the time spent by teams on managing these platforms can significantly increase the overall cost to a company. Research conducted by Aurora Analytica across four major CROs indicates that less than 25% of the time allocated for delivering a Request for Proposal (RFP) is spent on strategy development. With approximately 50-80% of that time being consumed by data wrangling and identifying ways to combine and leverage data in an actionable manner, leaving a surprisingly small amount of time for decision-making.²

As most platforms focus on specific aspects of the clinical trial life cycle, companies often rely on multiple platforms that function independently of each other. This leads to a "false economy" where organizations make significant investments in platforms without achieving truly integrated or cost-effective solutions. The disconnect between these tools results in high costs—both financial and operational—without delivering the streamlined, actionable insights required for effective decision-making.

Final thoughts

Clinical trial optimization remains a critical challenge for the pharmaceutical industry. The "holy trinity" of data, processing power, and analytics is central to success, but most companies struggle to combine these elements in a truly effective way. Although numerous data platforms exist, they often work in isolation, leaving key gaps in the trial life cycle. As highlighted by the Aurora Analytica survey, over 60% of respondents feel ill-equipped to leverage the data they gather, even though they rely on multiple platforms. Moreover, time and resources are spent on data wrangling rather than strategic decision-making, further complicating the optimization process.

The industry's reliance on multiple specialized platforms leads to inefficiencies and rising costs, which result in a "false economy." Companies make significant investments in tools that don't fully address their needs, leading to fragmented solutions. To truly advance, the pharmaceutical sector must find ways to integrate platforms, streamline data insights, and reduce reliance on isolated tools, ensuring that clinical trial strategies can be optimized both efficiently and cost-effectively.

Bhavish Lekh, CEO & founder, and Ola Gudmundsen, chairman; both with Aurora Analytica

References

1. Nvidia: https://www.nvidia.com/en-us/ai-data-science/generative-ai/
2. Aurora Analytica: https://auroraanalytica.com/
3. Citeline: https://www.citeline.com/