Clinical trials are becoming increasingly data-driven, with sponsors often expecting real-time data access and ongoing updates throughout a study rather than just a final report. This level of transparency requires the use of digital systems to support efficient data handling, analysis, and reporting for timely delivery of clean and accurate information. At the same time, artificial intelligence (AI) and machine learning (ML) are emerging as much-hyped new technologies and are gradually being integrated into clinical studies. But they remain tools rather than complete solutions, particularly in subjective fields like dermatology and rheumatology, where results are often based on human observations of outcomes, rather than quantitative measurements of disease biomarkers. How can companies running clinical trials improve their processes to take advantage of these ever-evolving digital platforms? What opportunities do they offer to improve data management and streamline operations to deliver high-quality results and maximize value for sponsors?
The drive to harness real-time data
Above all, clinical research is dependent on high-quality data to support evidence-based decision making. However, modern clinical trials are becoming more and more complex, producing vast data streams that must be analyzed and interpreted accurately. Errors or inconsistencies can cause costly delays, or even invalidate results leading to the failure of the project, making robust systems for capturing and integrating data essential.
Traditionally, clinical trials have relied on retrospective data analysis delivered in reports at the end of a study, however, if inaccuracies are identified or it becomes clear that there is inadequate data, then valuable time has already been lost. This approach also precludes the possibility of adapting or adjusting the protocol as a result of data emerging during the course of the project. In contrast, real-time data collection and analysis—made all the more feasible with the recent surge in digital health tools such as electronic health records, mobile health apps, wearable devices, and data analytics platforms—offers a continuous transparent stream of information that is swiftly becoming the expected norm for trial sponsors.
There is no doubt that the ability to access data in real time provides a number of advantages benefiting both sponsors and patients. The transparency it provides improves the quality and responsiveness of the trial since issues such as recruitment delays in certain demographics, protocol deviations, or preliminary data showing an unexpected adverse effect, can be identified and corrected earlier. This dynamic approach can help to make the process more efficient, potentially leading to quicker decision making, optimized use of resources, more ethical trials, and better patient outcomes. For CROs, providing this real-time access requires advanced data management systems that can securely integrate and share live data with sponsors without compromising data integrity or confidentiality.
Data integration to power analytics
One of the biggest challenges in collecting clinical trial data is that it relies on collating information from investigative sites that are geographically scattered, which can result in inconsistent data formats, incompatible systems, and dispersed data storage. These issues can lead to inefficiencies, delays, and potential errors in the protocol. When data is collected from across multiple sites, systems, and departments, it can be difficult to access and integrate information, leading to duplication of efforts and missed opportunities to correct anomalies. Effective data integration systems can significantly improve access and are critical to get the most out of the powerful data analytics tools that are now available. Data analytics itself plays a crucial role in operational efficiency, transforming raw data into actionable insights by identifying patterns and refining methodologies.
What is AI?
AI is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.
What is ML?
ML is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.
What is predictive analytics?
Predictive analytics uses data analysis, statistical models, ML, and AI to predict trends and behavioral patterns by discovering cause-and-effect relationships in data.
Source: Microsoft Azure - Artificial intelligence (AI) vs. machine learning (ML)
Where does AI fit in?
The power of analytics is being taken a step further with the advancement of AI-powered technologies such as machine learning and predictive analytics, however, it’s important to be clear about the differences between these methodologies and their capabilities to truly understand their potential—and their limitations—in the clinical trial arena.
AI tools certainly have the potential to help predict issues before they arise and optimize resource allocation. They also play a key role in processing and harmonizing the variety of data types collected in modern trials—from patient-reported outcomes and wearable devices to lab results and electronic health records—exposing patterns that can inform protocol design and execution. In addition, they can help to automate data cleaning and validation, allowing researchers to focus on high-value analysis instead of repetitive sorting.
While AI and ML are poised to revolutionize clinical studies in the future, they still have some way to go to be a complete solution. They already have the capability to analyze vast amounts of data, uncover hidden patterns, and generate valuable insights to enable data-driven decision making. However, at this stage there are still questions surrounding the quality and size of representative data sets that can be used to train these models. As the capabilities of AI and ML continue to evolve, and they become more integral to clinical trials, it is essential to maintain a balance between technological innovation and human expertise. AI should be deployed where it can genuinely add value, but human judgment remains crucial; AI lacks the contextual understanding necessary to make nuanced decisions about patient care or trial adjustments. To ensure the reliability of AI-driven insights, CROs should adopt a cautious, controlled approach to AI integration, prioritizing accuracy and accountability. This balance between innovation and expertise is vital for the integrity of clinical studies and reflects a commitment to patient safety and ethical research practices. This is particularly relevant in disease areas such as dermatology, where patient outcomes are based on subjective endpoints.
Process improvements to streamline data flows
Whether CROs rely on AI or not, the stream of data is the main deliverable, which is why improving its access, flow, and analysis is often a central focus for process improvement initiatives. For those that operate in niche indications—such as Innovaderm, specializing in the dermatology and rheumatology spaces—process efficiency is essential to remain competitive with larger players by delivering high-quality results while managing costs effectively.
Integrating any digital tools into a company’s SOPs requires careful consideration to understand exactly what can be achieved and how best it can be done. For example, by reducing the manual workload associated with data entry or patient recruitment, it is possible to free up resources that can be redirected toward specialized services, such as specific disease expertise. This focus on efficiency benefits sponsors, too, who receive timely, accurate results with the insights of subject matter experts.
Similarly, the use of integrated trial management systems enables cross-functional collaboration, ensuring that workflows are smooth and data flows seamlessly from one department to another, reducing silos and avoiding breakdowns in communication. By integrating data from trial management systems, electronic data capture, and other digital tools, it is possible to create a unified view of the progress of a study, feeding into collaborative efforts and reducing redundant data entry or analysis. Integrated systems are particularly valuable when working with external partners, such as investigative sites or sponsors, who need access to relevant data and updates. Building a robust infrastructure of integrated systems enables the delivery of high-quality, streamlined services that meet sponsors’ expectations.
Conclusion
Clinical trials are becoming increasingly complex and data intensive, and CROs must navigate a landscape that demands efficiency, transparency, and high-quality data. AI and data analytics are promising tools that will streamline operations and provide real-time insights to sponsors. Currently, AI in particular remains a tool and not a complete solution, with human expertise remaining central to the decision-making process. However, as the technology continues to evolve and improve, it is important to continue evaluating its potential in the clinical trial process. For CROs, particularly those operating in specialized fields like dermatology and rheumatology, the key to success lies in balancing technological advancements with operational efficiencies. By carefully integrating AI, improving data management processes, and fostering collaborative systems, CROs can meet sponsors' evolving needs, delivering accurate and high-quality data in a timely and efficient manner. As the industry advances, those who can achieve this balance will be well-positioned to deliver trials that benefit both sponsors and patients.
Guillaume Gigon, VP of Technology and AI, Innovaderm
