Joseph Lengfellner is Senior Director, Clinical Research Informatics at Memorial Sloan Kettering Cancer Center (MSK). Here, he talks us through what he sees as the biggest challenges facing clinical trials today.
Challenges of clinical trials
Clinical trials are really the last step before we deliver new life-saving treatments to patients. That’s a very exciting, innovative space to work in, hence, I’ve spent most of my career in the field. For around 15 years now, I’ve been immersed in the data, digital tools and technology that power clinical trials programs.
And, it’s safe to say, there’s no shortage of things to keep me interested throughout the day. The overall logistics of running a large clinical trials programme is very challenging and continues to get even more so. Let’s take a look at what some of those challenges (and emerging solutions) are:
Acquiring quality study data
One of the biggest challenges facing clinical trials at the moment is around acquiring high-quality data in the most efficient way possible. All stakeholders involved in the clinical trial process are interested in having access to good data, quickly. Yet, the way we currently acquire and manage data for clinical trials is a very labor-intensive, manual, error-prone process.
Recruiting for trials
Up to 80% of clinical trials are delayed or closed because of patient recruitment problems. The fact that such a huge number of trials never reach the point where the trial completes successfully is really a disservice to the patients that did participate in that trial, and represents a great deal of wasted time and effort for the sponsors, study sites and technology companies that put a lot of work into getting the trial open.
This has long been an issue in clinical trials, but it’s a problem that is deepening. As the number of clinical trials grow, there are more slots within studies to fill. And, more and more cost and effort are going into filling these slots so that trials can complete successfully.
Another big challenge for the industry is the sheer time it takes to execute a trial and bring treatments to market; typically, it can take many years to complete the process, in large part because of the issues already mentioned with recruiting patients for studies. It’s commonly quoted that every day a clinical trial is delayed can cost sponsors anywhere between $600,000 to $8 million. Add to this, the additional challenges for study sites running these lengthy trials, the burden on patients taking part, and – ultimately – the delay in potentially life-saving treatments reaching patients. Assuming all the necessary safety and efficacy data is collected, clearly shorter, more efficient studies would benefit everyone involved.
Of all the people involved in running a successful clinical trial, I believe the most challenging role is that of the research coordinator. Usually, this role is covered by a non-clinical research team member, responsible for the day-to-day management of the clinical trials. Often under-recognised for the amount they have to manage on a daily basis, this person (or people) is the lynchpin of the study, and the first point of contact for many queries that will come through from stakeholders including sponsors and contract research organisations (CROs).
Increasing numbers of trials means there is ever more need for this role. Conversely, across the industry we’ve seen this role becoming harder and harder to fill.
What connects these challenges?
You might expect me to say this given my role, but one thing really does tie all these issues together: data.
Study data quality
Existing methods for moving data into clinical trial systems lag well behind data transfer methods seen in other industries. Many thousands of hours are spent by research coordinators combing through both structured and unstructured clinical notes in medical records. At times, clinical interpretation – that they aren’t necessarily trained for – is needed. Then data is manually transcribed into the research system. This is not only laborious and time consuming, but it leads to quality issues.
We see about 7-8% error rates on that manually transcribed data, which then leads to a snowball effect of effort downstream. Data needs to be thoroughly checked through source data verification (SDV), errors identified and rectified.
Data and patient recruitment
For many patients, especially in oncology, a clinical trial offers the best chance at a good outcome. So, when a trial opportunity is presented to them, patients tend to be interested. Despite this, across the industry less than 5% of eligible patients will participate in a research study.
One major factor in how this issue is growing is that trial design is becoming more complex, with more trials focusing on targeted therapies. This leads to more detailed targeting of patients as the inclusion and exclusion criteria grow.
As a starting point, our current lack of easily accessible patient data means finding patients that fit the detailed criteria for these trials is becoming more and more challenging. But the challenge is not just around identifying the right patients. Just as importantly, the timing of when patients are approached is crucial. Most trials require patients to not be on another active treatment in order to be eligible. Equally, those that have progressed too far in their disease may be ineligible because of their performance status or other comorbid conditions. Solving the challenge of clinical trial recruitment is, in large part, about getting information in front of the right patient’s care provider, just at the time where the trial would be a good opportunity for them. Good data is, again, at the heart of this.
Data and trial length
Clinical trials are getting longer. Research from Tufts Center for the Study of Drug Development (CSDD) found that the clinical phase grew from 83.1 months in 2008-13, to 89.8 months in 2014-18. Anecdotally, my experience in the industry tells me that timelines have stretched even further since that analysis.
This goes back to the challenges of identifying suitable patients for the trial, as well as the increasing complexity of trials. More research from CSDD highlights that from 2013 to 2020, the number of phase 3 trial objectives grew by 17.6%. And the number of data points increased threefold in the last decade. Part of this trend is connected to higher expectations being placed on patients to complete more visits and procedures.
Ultimately, the complexity and quantity of these additional data requirements mean more research coordinators to manage the data and logistics of the trial and extensions of the timeline until completion.
Data and research coordinators
One of the primary responsibilities of a research coordinator is managing the data abstraction from the medical record into the EDC system. Keeping in mind that this individual will be acting as coordinator for several different trials, each potentially with different sponsors and different CROs involved, the technology we have in place for them doesn’t make their jobs easier. In fact, often the technology is actively making life harder, because each sponsor requires them to use a different platform, with a different login and different workflow.
I do believe that the way the industry uses data in clinical trials is a big part of why we’re seeing those increased challenges in recruiting and retaining people in this vital role. Not only are research coordinators overburdened by the amount they’re expected to do, but they are spending huge amounts of time copying and pasting data. It’s unsurprising that many think “This isn’t what I signed up for”.
Thankfully these challenges are not insurmountable. For a long time, we’ve known that if we had access to better, more structured data from the source, we would be much better equipped to tackle the challenges I’ve outlined. We could have better tools to help automate the patient recruitment process. We could manage the data we need from electronic data capture (EDC) systems in a more efficient way. We could design trials where we know we have a patient cohort ready to go, and fill those slots. Research coordinators could spend less time on the manual task of wrangling data and instead do things like helping identify patients that might be a good fit for a clinical trial, and working with those patients once they are on a study.
Now, we’re finally beginning to see technology solutions emerge that offer the promise of a real step change in clinical trials. A few of the latest and emerging solutions worth mentioning are:
- Common data standards like FHIR and the use of RESTful APIs that are powering a huge shift in the user experience of common healthcare systems.
- EHR-to-EDC data transfer technology, which is transforming the way we can re-use clinical data for clinical trials.
- Artificial intelligence (AI), including natural language processing and machine learning, which offer real promise for opening up access to data currently buried in unstructured notes.
- Increasing recognition that we need to modernise the way we document our clinical trial protocols, to enable a faster transition from proposal to ‘go-live’.
Some of these solutions have already been proven, some feel just within our grasp. Join me next time for part two of this blog, where I’ll talk about them in more detail.
(The opinions offered during this interview reflect Joseph Lengfellner’s views only and do not represent those of MSK.)
IgniteData has announced a new collaboration with Memorial Sloan Kettering Cancer Center (MSK) – read more
IgniteData is transforming the future of clinical trials through its cloud-based Virtual Research Assistant, Archer. A system-agnostic solution, Archer brings modern interoperability between EHR and key research applications, such as EDC. Providing seamless, secure transfer of clinical data, Archer is the global EHR-to-EDC solution for modern clinical trials.
If you would like to get in touch, please use our contact form.
Senior Director, Clinical Research Informatics & Technology
Memorial Sloan Kettering Cancer Center (MSK)
How to choose an EHR-to-EDC solution
Leading healthcare technology expert Steve Tolle joins IgniteData
Guest blog: Joseph Lengfellner on the challenges and solutions for clinical trials – Part 2
How structured data is used in clinical trials
Evidence from Electronic Health Records-to-Electronic Data Capture live pilot study
IgniteData and Leading New York City Cancer Center Collaborate to Solve the Clinical Trial Data Transfer Challenge
Clinical trial data mapping explained: Mapping EHR data
Quality data and the EHR-to-EDC dream – where and why we need to focus our energies
UK clinical trials landscape: 4 big influencers
ZS’s Qin Ye on trends and driving innovation in life sciences
Meet Archer, the platform transforming EHR-to-EDC data automation