Clinical Trials: Are We Any Closer to Solving the Rubik’s Cube?
We need to examine the total experience in clinical trials—of users, not just data—if we want to achieve true operational excellence.
Clinical trials were one-dimensional when I began my data management career more than 20 years ago. Decisions were made in a linear fashion. Database lock was treated as sacred and required multiple permissions forms to reverse. The phrase ‘data veracity’ didn’t exist in our vocabulary because every data point was born equal.
Today, we’re in an era of platform, bucket, and umbrella studies. Companies are exploring trials with an immense focus on individual visits and data points, running studies that require amendments almost after every patient visit. As Tanya du Plessis, chief data strategist and solutions officer at Bioforum, notes, “We’ve gone from linear to almost circular [in data management]. It’s hard to determine where one step starts and the other ends, and that’s not even accounting for the volume or veracity of data”.
Scientific developments have been accompanied by an explosion in data complexity. In the old days of paper-based clinical trials, sites captured 90% of clinical trial data on paper. Today’s electronic data capture (EDC) now typically contains only 25% of the overall picture, with the remainder coming in from ePRO, labs, imaging, genomics, and other third-party sources. As an industry, we lack the systems and processes to manage non-conventional data at this kind of scale.
Defining the roadmap
Many sponsors are currently translating their vision for 2025, or even 2030, into a roadmap in which improved efficiency is the ultimate destination. However, there is an equal and potentially opposing force at work here: scientific rigor. The harder we push in one direction, the less successful we might be in the other.
We can accurately describe our trial execution plan as a pendulum on a string: the further you reach into the scientific world, the harder operational excellence becomes, and vice versa. We must realize that solving problems for one user group can have unintended consequences on others that could move their goals out of reach, causing the pendulum to swing the other way.
For instance, patients have benefited from new digital ways to engage in trials since COVID-19. However, sites now report seeing 50% fewer patients than their pre-pandemic patient volume. Site users are struggling to adopt new technology to manage data collected from multiple offsite sources. Doug Bain, chief technology officer of KCR, summarizes: “We’ve almost gone through a curve, where we started with paper, throwing more and more technology at it, and we’ve created a problem in itself. There’s an avalanche of systems chucked at sites”.
By swinging the pendulum to meet one need (patient engagement) we have seen an equal and opposite, negative impact on another key user—in this case, sites. Myriad similar tradeoffs exist.
The Rubik’s Cube of today’s clinical trials
I believe we’ve reached a point where clinical trials are like a Rubik’s Cube, with each of the six sides representing an important user. These include (but are not limited to) patients, sites, data management, clinical research, regulators, and pharmacovigilance.
One of the reasons I like this metaphor is because when you put down a Rubik’s Cube, you can only ever see three sides. To see the rest, you either have to pick up the Rubik’s Cube or physically change your own position. If you, as an individual, make a change to try and solve the overall puzzle, you can’t see the full impact on other people. Then, introduce the fact that multiple players (or functions) are trying to solve different challenges simultaneously. The result? Multiple people make changes, some benefit all users, while others only benefit a few. Many have a negative impact on one (or more) users, of which they have no visibility.
In fact, I would suggest that instead of solving the Rubik’s Cube (i.e. making everyone’s trial experience easier), we’re actually building a more complicated and bigger Rubik’s Cube, nine- or even 16-sided, with each uncoordinated move.
This is the challenge we face today in clinical trials. And just as no one can solve the Rubik’s Cube with one move, the challenges we face are too big to solve in one go. Electronic health records (EHR) and electronic medical records (EMR) are prime examples. At first glance, there is tremendous potential to pull data from EHR/EMR records, but the complexities of that request are challenged at every level. Site-specific implementation decisions create inter-site and intra-site complexities that will challenge our data requirements for years to come.
Today, we can probably address these challenges for one study, one site, or perhaps one department, but operating at scale is beyond imagination. This doesn’t make the goal the wrong one. But it highlights the need to approach a multifaceted problem one step at a time.
The true value comes from taking incremental steps along the path to innovation. We need to be smarter in how we think about technology and focus on easing the burden of daily, time-consuming tasks. Perhaps we can identify a problem that needs to be fixed at the source (like data entry). Or we could focus on a process, or outcome, that is inhibiting us, like manual reviews or single-use data. We need to take the stepping stones instead of waiting to solve every problem.
Activities like data cleaning, medical coding, safety signals, and predictive analyses are all too often done on paper or in spreadsheets. Automating a manual process, like end-of-study data or serious adverse-event reconciliation, would make the data manager’s day job significantly easier. It requires better technology and the intelligent use of data, rather than a transformational leap to AI or machine learning. This is where the analogy proves helpful again. You may get to a point where you’ve solved two sides of the Rubik’s Cube. Once these two sides are complete, you think you are close to completing the challenge. However, the only way to solve the rest of the puzzle is to carefully dismantle the two resolved sides. By taking a step back for that side (or user), you can let another side advance. This is where AI/ML surely has a role to play. An intelligent workflow will enable a 360-degree view of the world.
Indeed, there are everyday use cases where AI/ML can have a major impact. Trevor Griffiths, senior director of clinical data management at CRO Syneos Health, notes that machine learning has reduced the manual data cleaning effort by 3,000 hours in a single large trial. There are exciting things we can do outside of clinical data management software—pooling data, analyzing it for patterns, and sharing actionable insights with the right user, to name but a few.
Stepping stones instead of a bridge
Technology can accelerate our efforts as an industry, but it also has to balance a series of competing forces: operational excellence with scientific rigor, innovation demand with regulatory demand, and patient- and site-centricity with data complexity. We have to accept that not all decisions will work out, but positive steps forward happen when teams have space to experiment.
We must also be brave enough to make changes, knowing that some will not deliver the positive results we are seeking. Evidence comes through experience and experience comes through doing.
Data is the foundation of clinical trials and a solid approach to data management is critical to clinical trial success. By treating each move as a step forward on that path, we will reshape the options ahead of us. In this scenario, progress means discovering the value that we cannot yet realize and making that our destination. Eventually, we may even solve the Rubik’s Cube.
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