How Can Unified Trial Management Future-Proof Clinical Trials?

In a recent interview with International Clinical Trials, our Chief Product Officer, Ricky Lakhani, explored the shifting landscape of clinical trial management and what it takes to stay ahead. With growing trial complexity, tighter timelines, and mounting regulatory pressures, the conversation zeroed in on one critical theme: unification.

Unified trial management solutions aren’t just a convenience—they’re becoming a necessity. Our CPO shared how integrated platforms can streamline operations, reduce risk, and give sponsors and sites the agility they need to adapt, scale, and thrive in a rapidly changing research environment.

Read the full feature to see how we’re helping future-proof clinical research.

Why do trial management solutions need to be unified?

Clinical research is in a period of hypergrowth, and the trial paradigm has shifted since initial clinical trial management systems (CTMS) and other early eClinical technologies were introduced.

In this complex environment, unification is a necessity to improve data consistency, reduce data duplication, increase automation of mundane tasks, with and without the use of artificial intelligence (AI), improve the overall quality of trial execution and reduce staff attrition.

By taking a unified approach, trial leaders can access centralised master data, which can be referenced across multiple processes, systems and studies. A unified approach improves data consistency and accuracy, enhances data accessibility, empowers better decision-making, and increases traceability and real-time data access.

Using technology to unify fragmented systems and processes also allows teams to collaborate more effectively as they all have access to the same, consistent data. Unified and automated processes throughout the trial execution can also reduce timelines and costs, and improve overall study quality.

A unified model simplifies data management tasks, such as data cleansing, transformation and storage.

This creates a single source of truth for data, making it easier for users to access and share information.

What are some of the key challenges that need to be overcome?

A lack of unified approaches to clinical trial management can lead to several challenges. These include:

• A lack of sponsor control over the technology used by a clinical research organisation (CRO). This can limit sponsor oversight and ownership of the data and make them reliant on CROs to gain the necessary information through reporting and update

• Technology itself becoming a burden rather than making people’s lives easier. For example, having two spreadsheets and then moving to two technology systems does not lead to improvements because you are still working in two (potentially) fragmented systems, with two different interfaces rather than using a unified approach

• Different systems with different data models and different processes requiring costly integrations to implement and maintain. In some instances, it may not be possible to integrate systems

• A lack of truly unified systems. Many vendors will say they have unified technology, but in reality, they are still individual systems. They are not truly unified with a single data model and therefore not a truly unified processes. This means there is still work to do to make the two systems talk to each other, which can lead to delays and costly integration

• Expensive systems. Where unified technologies do exist, they are generally more expensive. This limits availability for small- to medium-sized organisations which can be priced out of these enterprise-level systems

• Resistance to change and acceptance of slower initial timelines. Changing from spreadsheets to a technology system will slow you down initially. This can be hard to accept as many companies are looking through the lens of a single study. If, for example, you have a single product that you are trying to get through to approval, it is tempting to maintain the status quo rather than adopt technology which is potentially going to slow you down and take 12-24 months to start realising a return. It can be difficult to accept you have to go slowly initially to go faster in the medium term.

The increasing complexity of clinical trials adds changing the requirements for unified systems and how they interplay. One of the key things the industry wants to get out of unified systems is data standardisation.

As trials get more complex and there are different variants like adaptive trials, systems need to be more flexible and it must be possible to bake in standards for different trial types. Attention must be paid to the commonalities between trial types to create reusable systems.

How can we simplify data collection and streamline site management?

The adoption and promotion of data standards is, and will continue to be, crucial. For example, the Clinical Data Interchange Standards Consortium (CDISC) Trial Master File (TMF) Reference Model gives companies a global standard to work from. There are always going to be small tweaks that people want to make, but this gives a starting point which was lacking before. As it evolves to version four and embraces practices such as controlled terminology, it will mean technology providers can offer a baseline that is an industry-wide standard, irrespective of the provider. This is likely to be particularly useful for smaller organisations that will evolve from an industry standard over time rather than having to start from scratch, and benefit from greater choice in the marketplace.

Data standards are needed to be pushed in different areas, and vendors must not only adopt, but promote them. This will make the interplay between technologies easier, offer more choice to the market and bring integration costs down.

Sponsors need to use the right trial management technology, partnered with a native electronic data capture (EDC) integration, to allow seamless data sharing between systems. This approach can empower the entire study team to make timely, informed decisions and allow teams to manage all aspects of global site and investigator monitoring, contracting and payments from a single place. Site management solutions can also expedite initial study set-up with standardised and configurable organisation assessments, site contracts and reports. Teams can build a knowledge base about recurring sites and investigators by accessing site performance history and setting up automatic payments. Once trials are in motion, unified site management solutions help simplify formerly complex or manual efforts for study teams, such as dealing with multiple versions of site contracts.

Increased awareness of site performance also helps clinical research associates (CRAs) identify and mitigate issues quickly, reducing the potential study impact and strengthening the CRA-site relationship. When teams have better oversight into site performance, they can make more proactive and data-driven decisions. This streamlined approach to study management is more sustainable and scalable for teams looking to grow with the industry. Beyond the operational benefits, the right trial management technology can unlock new business intelligence, increasing overall productivity and ensuring critical-to-success issues are identified, explored and addressed swiftly.

How can diverse teams be connected?

Successful studies rely on how seamlessly teams can communicate.

Investing in unified eClinical solutions creates a single source of truth for dispersed teams and allows people working anywhere in the world to access the same data in the same way, while also accommodating language and time zone preferences. This removes harmful silos and empowers informed, timely decision-making.

When EDC data is automatically reflected in trial management software and vice versa, all teams have access to the same information at the same time. Teams can centralise protocols in a structured manner and create more standardisation and collaboration around the study management process. Instead of forcing employees to swap between multiple platforms or wait on manual reconciliation, interoperable solutions unlock faster access to trusted study data.

Built-in audit trails and user restricted access add an extra layer of security and control around sharing data across dispersed teams. Regulatory submissions are also made easier by removing silos, automatically tracking actions and expediting information sharing for inspection documents.

How can CTMS be further enhanced?

CTMS already hold a lot of quantitative and qualitative data on sites, including how they have performed, historic enrolment, protocol deviations etc. If there is a unified eTMF that is natively a modular component of the CTMS, then there is an argument to say all study start-up activity – in terms of modelling for site selection feasibility and document package green light – could sit within the umbrella of a trial management system.

Looking further upstream in the life cycle of a study, risk management planning and risk assessment could also be part of trial management. This will require an interplay with other systems in the risk-based quality management (RBQM) space, however, there are currently separate systems for initial risk planning assessments and then operational activities, but they do not feed back in an automated way unless there are two integrated systems. If risk planning and assessment lived within the trial management domain, the clinical data aspects would still happen outside of the CTMS, but risk planning and ongoing risk management could happen within trial management.

What should trial operators consider as they look to the future?

There are two key questions trial operators should consider: how can I capture data in a way that is going to be reusable when I need it, and how can I learn from the data that is being collected? It is necessary to learn from data that is being collected in a way which is clean at the beginning and does not need to be cleaned and standardised later. It is a bigger challenge to retrospectively clean data to make it reusable for continuous learning and forecasting than it is to capture data in the right way from the start.

While we cannot ignore AI, we should not lead with it. All the systems we create should be focused on making sure the data we capture is usable to identify and solve future problems, rather than trying to build tools using AI before we have the necessary data or an identified problem. We need to start at the beginning, capture the data in the right way, understand how we might need and reuse that data, and then build tools on top of it.