Welcome to the third annual Industry Outlook.
01) Executive Summary
Last year, we learned the clinical research industry was experiencing a substantial surge of interest in AI, driven by increasing trial complexity and data challenges.
This year we wondered: How are organizations preparing for the future of clinical data, and what role is technology (particularly AI) expected to play in this transformation?
Organizations continue to experiment with many AI use cases, but we aren’t seeing the same fervor as last year. Fewer report being in the exploratory phase of AI adoption, while there is an increase in both those who report having integrated AI across multiple applications—and those who have no plans to integrate AI.
Also noteworthy, organizations are increasingly focused on risk-based strategies, which identify, assess, and mitigate potential risks to ensure trial quality and compliance. In both cases—and in the case of many projects biotech and pharma companies are working on—there is significant opportunity.
While technology plays a critical part, our data reveals an increased recognition that organizations need the right processes and people in place if they want to succeed.
As organizations prepare for the future of clinical data and AI, their mandate is clear: Focus on your processes, capabilities, and attitude toward risk, and your technologies can be used to harness the potential of everything that is possible now—and in years to come.
RISK-BASED STRATEGIES
AI continues to be the most important trend in the coming year. While 72% of respondents are using or considering it, AI is still in the experimental phase as respondents continue to use it for a range of use cases with no specific application standing out.
ARTIFICIAL INTELLIGENCE OUTSOURCING
The majority of organizations, 75%, use outsourcing partners to manage their clinical trial data, but those partners often fall short. Respondents are especially interested in working with partners who help with innovation and real-time data access.
REAL-TIME ACCESS INNOVATION
Providing real-time access to data across clinical teams is a high priority for 59% of clinical data professionals. Teams are making progress, though: two-thirds of respondents report they have centralized access to their data.
Risk-based strategies are a rising trend, with nearly 2x the respondents noting risk-based strategies as poised for the greatest impact on efficiency and outcomes in the next 12 months, compared to last year’s survey. However, teams are not set up for success, as only a third report their tech stack is effective at managing risk in clinical trials.
Key Takeaways
Organizations are slow to innovate. Only 26% are very satisfied with the speed at which their organizations are innovating their data strategies and processes.
85% are somewhat confident they have the right tech stack in place, but only 41% are extremely or very confident. However, more oftenreported reasons for this are related to the organization’s processes and technology adoption methods, not the technology itself.
the Survey
The survey was fielded online by eClinical Solutions in the fall of 2024 via events, email, and social media.
What best describes your organization?
What
Mid-sized biopharma
Large biopharma
Small biopharma Contract
What best describes your role?
How many years have you worked with clinical trial data?
(03) Priorities
What priorities are most important to them right now?
When asked to rate the priority of common activities, respondents placed “ensuring data quality” at the top of the list. This reflects the reality that quality data is table stakes, impacting all parts of the process, including creating submissions and leveraging new technology such as AI. In short: As the core currency of clinical development, quality data will always be the priority.
We believe that what our survey revealed as the second-highest focus—getting real-time access to data—is more illuminating.
This level of insight is crucial, because it directly supports the goal of ensuring fast, informed decision-making and enabling collaboration across all stakeholder groups. Additionally, real-time access enables organizations to streamline trial operations, comply with regulatory guidelines, and assure patient safety. It’s increasingly critical in the context of adaptive and innovative trial designs that rely on interim analyses.
How are organizations currently prioritizing efforts to address or solve the following challenges?
The good news is that many organizations are making strides. More than two-thirds of respondents report their organization has a method
to centralize data across all of their sources. And eClinical Solutions customers are 32% more likely than their peers to have centralized data.
What trends are clinical data professionals focused on?
ARTIFICIAL INTELLIGENCE (AI)
RISK-BASED STRATEGIES
While it’s useful to understand where organizations are currently prioritizing their efforts, we also wanted to look forward. What trends are clinical data professionals focused on?
Two things topped the list: AI and risk-based strategies.
As we saw last year, AI is the trend most respondents think will impact efficiency and outcomes.
Compared to last year, there is an increase in those who report they have integrated AI across multiple applications—and an increase in those who have no plan to integrate AI.
Which life science industry trend do organizations believe will have the greatest impact on efficiency and outcomes over the next 12 months?
One possible explanation for this shift is the “hype cycle,” which is a conceptual model developed by Gartner. The model describes how emerging technologies typically follow a predictable pattern: initial enthusiasm with outsized expectations, followed by a period of skepticism as the technology’s limitations and barriers to adoption become apparent.
Last year, AI was at the peak of inflated expectations as respondents expressed widespread excitement about its transformative potential. This year, we believe that initial excitement may have waned as organizations have deepened their understanding and are becoming more realistic about what is needed to use AI effectively.
Compared to last year, more organizations have integrated AI across multiple workflows...but fewer are considering it.
What is needed to use AI effectively?
What challenge(s) do organizations expect to encounter as they adopt AI to better manage clinical trial data?
This year, we asked those who are using or considering AI to share their challenges. Unsurprisingly, the vast majority (98%) are facing at least one challenge. (On average, respondents identified 2.5 pain points.)
As we analyzed the data, a trend emerged: Respondents are more challenged by the process of using AI than by the technology itself.
Respondents are uncertain about process-related considerations such as regulatory compliance, data privacy, and how to prove ROI. Said another way: Many organizations are in the early stages of figuring out how to adopt AI. They are often focused on the foundational aspects of thinking through how this will work before they start doing the actual work.
Challenges in training and upskilling personnel
Data privacy and security concerns
Resistance to change / adoption
Uncertainty about regulatory compliance Lack
We also asked organizations which AI-enabled use cases they have integrated or explored. A solid third (34%) were uncertain, which indicates how new AI is within clinical development.
Of those who had a better understanding of how their organization uses AI, there was little standardization in terms of usage. This is very similar to what we uncovered last year, with no specific applications emerging as frontrunners.
Even though the industry is at an inflection point and there is no “right path” to using AI, the potential for meaningful change through strategic adoption across the clinical development life cycle is enormous. We fully expect to see AI transform how data is managed, analyzed, and applied across clinical trials in the coming years. These rapid technological advances give us the opportunity to rethink traditional workflows, embrace automation, and achieve efficiencies that were previously unimaginable.
Which AI-enabled use cases have been integrated or explored within organizations?
RECOMMENDATION
Establishing an interoperable data and analytics infrastructure is foundational for unlocking the full potential of embedded AI and other advanced capabilities. A strong, well-designed foundation accelerates value realization and empowers organizations to keep pace with innovation.
While viewpoints about the promise of AI were relatively consistent compared with last year, another trend stuck out because of its year-over-year growth. This year...
This figure is almost double what was reported last year (13%).
Why the uptick in interest in riskbased strategies? We believe it’s because clinical data professionals increasingly recognize the importance of prioritizing efforts where they make the most impact, to not only improve efficiency but also ensure that organizations can effectively scale in the context of escalating complexity.
Risk-based approaches represent a significant mindset shift for organizations and stand to play a vital role in equipping teams for the ongoing growth in data volume and variety.
While we applaud organizations for this increasing recognition of the importance of risk-based strategies, our data shows they are not set up for success. Only 36% believe their tech stack is currently extremely or very effective at managing risk.
The pathway towards risk-based quality management (RbQm) can be daunting for organizations as they face challenges with change management, skills development, regulatory uncertainty, and technical infrastructure. To ease the transition towards full RbQm integration, consider a phased model approach.
begin by establishing Critical to Quality (CTQ) factors and basic risk management protocols, then progressively add oversight and centralized monitoring capabilities. by taking incremental steps, organizations can achieve quick wins while building the necessary technology infrastructure and processes to set the stage for more comprehensive RbQm implementation.
RECOMMENDATION
(04) Processes and Tech
Do organizations have what they need to succeed?
One common question pharma and biotech companies often ask is, “Should we build or buy the technology we need to manage our clinical data?”
We learned that the vast majority (93%) of organizations are buying some, or all, of the tools in their tech stack.
However, we do see some differences based on company size. Mid-sized biopharmaceutical companies are more likely than their peers to buy technology, while large organizations are more likely to build some elements of their solutions.
RECOMMENDATION
This aligns with the many conversations we’ve had with industry experts. While there are always specific cases where building is the right choice, many believe buying technology is often the better path because there are many available, proven solutions that offer what companies need. Additionally, organizations often underestimate the long-term costs of managing custom solutions, including ongoing maintenance, security, and innovation.
Consider buying proven solutions and building only when there’s a clear white space or unique requirement.
The majority of pharma and biotech organizations buy some, or all, of the tools in their tech stack
Regardless of whether an organization is building or buying their tech, we wanted to understand how well their tech stack is working.
At first blush, our findings indicate there may be an issue with technology: Only 41% are very or extremely confident they have the right integrated tech stack in place to manage their clinical data.
However, when we dig in to understand why the tech stack isn’t working as well as it could be, another story emerges. The primary reason organizations’ tech stacks aren’t working as well as they could is not because of the technology itself. Rather, respondents are challenged with the processes they are using to adopt, implement, and use the tech.
Why do organizations think their tech stack is not working as well as it should be?
We categorized the challenges into three buckets: people issues, process problems, and technology limitations (Note: some challenges were included in multiple buckets.)
We then found that 82% of technology limitations are related to a process problem, such as lack of a standardized process, systems that don’t work well together (interoperability), or lack of change management to support adoption.
Additionally, only 28% of respondents believe their organization is extremely or very effective at adapting its processes when adopting new technology. These findings underscore the importance of taking a holistic approach to innovation and technology integration: bringing together people development and process optimization alongside a focus on the technology itself.
72% of organizations struggle to adapt their processes when adopting new technology while only of respondents believe their organization is extremely or very effective at adapting its processes when adopting new technology
% innovate. Only 26% are extremely or very satisfied with the speed at which their organizations are innovating their data strategies and processes.
Why is this happening? It’s hard to pinpoint one single reason
based on data and industry conversations, it seems likely that organizations are struggling to innovate for several reasons
Inability to make the business case and prove ROI for innovations.
Perhaps not surprisingly, in the context of biopharma’s ongoing struggle to meaningfully accelerate cycle times and boost R&D productivity, we also learned organizations are slow to
Teams may have become jaded as a result of unrealized hype and previous initiatives.
Lack of resources.
Operational activities take the focus and data strategy innovation is not sufficiently a priority. (Organizations only put budget toward the things they think matter.)
Why do individuals think their organization is struggling with innovating its data strategies and processes?
RECOMMENDATION
If you want to improve how your organization innovates and transforms its clinical data infrastructure, consider the following approach based on our team’s experiences:
Start by identifying the specific goals for transformation and prioritize the area of greatest impact and reward. Next, launch a pilot project to build familiarity and develop best practices before scaling up. It’s useful to involve multiple teams and collaborate cross functionally because innovation requires multiple perspectives.
Don’t forget to measure the impact of your project and trace metrics back to your initial goal. As you do this, you’ll be able to better assess which projects to focus on in the future.
Lastly, as you scale the project, focus on training and upskilling teams based on what you’ve learned so everyone is prepared for both current and future challenges.
(05) People
are organizations getting the support they need?
75% of organizations outsource some, or all, of their clinical trial data management
Our data shows there is no one right way to get support. 75% of organizations are outsourcing some, or all, of their clinical trial data management.
The most common path is a hybrid model that uses a mix of full-service (full study implementation), functional service outsourcing (specialized partnerships by function), and contractors.
No matter the model, outsourcing some or most clinical data management and biostatistics activity is routinely expected for biopharma organizations of all sizes, with internal resources deployed on overseeing these partnerships and deliverables. Outsourcing strategies vary depending on an organization’s size and how they prioritize a range of objectives such as flexibility, scalability, cost efficiency, and access to specialist expertise. But our research indicates that clinical data outsourcing partners are falling short.
Which activities are outsourcing partners doing well— and what could they do better?
Delivers high-quality outputs
Assigns experienced professionals to our projects
Communicates proactively
Uses advanced technologies
Provides real-time data insights
Proposes innovative ways of doing things
Meets clinical data timelines
The majority of our survey respondents’ outsourcing partners aren’t even fulfilling the basics such as
timelines
Delivering high-quality outputs
Assigning experienced professionals meeting clinical data
What’s more, 80% of those who are outsourcing report they want their partner to do better at providing them with real-time data insights. (As you recall, this is one of the top priorities identified this year.) And 81% want their outsourcing partner to do better at proposing innovative ways of doing things.
The data points to a clear disconnect between the growing complexity of clinical data and traditional outsourcing capabilities and approaches. Historically, in the context of clinical outsourcing,
budgets were weighted towards the clinical monitoring aspects of the trial, and data didn’t always get the attention it deserved.
As volume and variety of data have escalated, it’s clear that outsourcing providers need to consider how to equip themselves for today’s new challenges by deepening their data expertise, taking a proactive approach to building innovative solutions, and integrating technology that delivers real-time insights for their clients.
Sponsors should consider how specialized models that put data front and center can impact the overall outsourcing strategy. When evaluating vendors, ask them how they are transforming their data delivery—across people, process, and technology—to efficiently handle more complex trial designs and emerging data types.
It’s also critical to understand how they are measuring success with KPIs and are targeting improvements. For instance, are they working to achieve acceleration across key cycle times such as Last Patient Last visit to Database Lock?
(06) Future
The following themes emerged
AUTOMATION AND TECHNOLOGY ADOPTION
There’s a strong emphasis on automating routine processes, adopting advanced tools (such as AI and mL), and integrating new technologies. Respondents hope these measures will reduce manual work, streamline data handling, and improve efficiency and accuracy.
REAL-TIME INSIGHTS
As discussed throughout the report, many want to make data more accessible across teams and available in real-time. Real-time insights help organizations respond quickly to issues, optimize trial outcomes, and improve decision-making.
ENHANCED DATA INTEGRITY AND OVERSIGHT
Respondents express a desire to ensure data accuracy, oversight, and compliance, possibly for regulatory and quality assurance purposes. This includes managing data integrity and implementing oversight mechanisms such as audit trails.
SIMPLIFICATION AND TRANSPARENCY
Many respondents hope to see simplification in processes, fewer manual steps, and avoidance of “piecemeal” approaches to change. This reflects a preference for operational efficiency and clear communication across teams.
Themes
STANDARDIZATION AND CONSISTENCY
many responses mention the need for creating consistent processes, standardizing workflows, and implementing universal standards with the goal of enabling consistency across trials and improving cross-functional collaboration.
CHANGE MANAGEMENT
A few responses highlight the need to encourage adoption of—and address industry resistance to—innovation.
INTERDEPARTMENTAL COLLABORATION AND TRAINING
Enhancing collaboration across departments and improving training on new technologies is mentioned. The goal is to ensure all teams are aligned and equipped to use the tools effectively.
Looking Ahead: A Note on AI from Raj Indupuri, CEO of eClinical Solutions
RAJ INDUPURI CEO, eClinical Solutions
Technology, most notably AI, continues to change at an unimaginable pace. Enterprises must embrace reinvention rather than incremental transformation to leverage these opportunities. We are amidst an AI super cycle, where capabilities are evolving at an unprecedented pace. Applied effectively, AI can solve significant industry challenges. In clinical data management, AI can be integrated from acquisition to submission and insight generation.
Unlocking AI’s potential requires embedding it pervasively across the clinical data lifecycle. broader improvements will not come from AI by automating discrete processes within the entire value chain or by implementing incremental changes. Life sciences R&D leaders must take a step back and look at the possibilities with a reinvention mindset. This strategic shift necessitates a fundamental redesign of workflows, integrating AI capabilities across the entire value chain to close the gap between AI’s potential and existing processes. Taking this approach could finally unlock the value of widespread cycle time reduction.
About eClinical Solutions
eClinical Solutions’ industry-leading data & analytics platform, elluminate, and biometrics services experts help biopharma researchers at large, mid-size, and emerging life sciences organizations manage trial complexity in less time and with fewer resources. Clients get accurate and timely data insights for better decision-making—enabling them to reduce cycle times, improve productivity, easily scale, and develop tomorrow’s breakthroughs with today’s resources.
