The pandemic has served as a tipping point to advance decentralized clinical trials (DCTs) and other innovations in clinical trial management. To date, most of the innovation has centered around infrastructure and process—solving for the patient burdens and geographic barriers related to time, travel and logistics. Meanwhile, there’s been much less innovation on the other side of the equation: patient recruitment, the linchpin of clinical research.
In these early days of DCTs, there’s been a general mindset of “if we build it, they will come.” However, there haven’t been corresponding efforts to get patients to come. We aren’t seeing many new education and awareness campaigns designed to target the historically hard-to-reach personas for which DCTs are primarily intended. Moreover, while trial designers often attempt to keep the patient experience top-of-mind, they frequently fall short due to the errant assumption that they already understand the patient rather than working through outreach to advocacy groups, trusted community providers, or the patients themselves.
according to Statista
A human-centered approach focuses on understanding where people are and meeting them there—not just physically, but also mentally and emotionally. DCTs help solve for a portion of the physical part (~70 percent of potential patients live more than two hours away from existing study centers). What’s needed now is a targeted approach to meeting patients where they are mentally and emotionally to guide them through the process of finding, understanding and enrolling in a clinical trial.
The good news is there’s no need to reinvent the wheel. Recruitment efforts can take a page from marketing playbooks of other industries to adopt best practices—such as developing personas to understand the diverse set of patients desired to tailor messaging or participation options to them. Those personas can also guide the creation of new, trusted channels to inform, educate, and recruit a wider audience, including patients outside the reach of mainstream research sites.
Using trusted channels such as local doctors’ offices, community clinics, pharmacies, churches, workplaces or community organizations, can help overcome awareness and education barriers. Less than 50 percent of U.S. adults have ever heard of a clinical trial, and less than 43 percent have ever seen an advertisement for one, according to Statista. What’s more, building a network of local influencers within those channels can help overcome some of the misconceptions and trust issues that have hindered participation in the past.
Other industries have developed digital marketing best practices in awareness-building and lead conversion that can be adapted and applied to trusted channels as a way of meeting patients in a place that’s familiar to them both physically and digitally. Other healthcare sectors have started to do this and have also opportunistically used the pandemic to increase the personalization of content. For example, we’ve seen large retail pharmacy clients provide personalized healthcare/pharmacy suggestions based on location and recent customer activity by deploying mobile apps that provide push notifications related to COVID testing/vaccine services, pollen counts/allergy meds, etc. These personalized marketing tactics are advertised using paid media channels and, in some cases, delivered by celebrity influencers. While behavioral outcome results from these activities have been slow to evolve, an increase in health and wellness engagement is expected. Clinical trial sponsors have an opportunity to latch onto these trends and the recent clinical trial awareness caused by the COVID-19 vaccine trials to employ similar tactics. When they do, they can likely use influencers or trusted voices in the various channels mentioned above rather than needing to lean on outside influencers.
Meet patients where they are
There’s little argument that patient recruitment has historically been the single biggest challenge in drug development. If you've heard about any statistics surrounding clinical trial recruitment, it's most likely that approximately 80% of clinical trials are delayed or closed because of problems with recruitment. Additional statistics paint a troubling picture: Delays can cost sponsors between $600,000 and $8 million for each day that a trial delays a product’s development and launch. This last statistic alone points to why the pandemic shifted the use of DCTs from a nice-to-have efficiency gain to a financial risk management must-have.
Now that innovative digital technology and supporting processes have been deployed to support the paradigm shift to DCTs, recruitment tactics must also shift so that patients can realize the benefits from this increased access to novel treatments. To overcome historical barriers such as lack of awareness and education, safety stigmas, or fear of the unknown, what’s needed is a much more proactive, human-centered approach. In the same way that DCTs used, collated, and refined existing technology to drive innovation, a new wave of recruitment tactics can follow a similar path to innovation: applying existing marketing and education best practices focused on human-centered design.
Disease groups are inherently heterogeneous; however, clinical trial populations typically are not (e.g., ~72 percent white), according to a report from the FDA. This disconnect highlights the challenge of recruiting a study population that matches the real world. A human-centered approach can help overcome this constraint by reaching, recruiting, and retaining a more diverse mix of potential participants.
Designing for flexibility and inclusion is integral to meeting patients where they are. By creating and applying diverse personas as outlined above, clinical trials can be designed in a way that’s inclusive to multiple personas—not just catering to the most common participant type. Using trusted channels to recruit, together with the new pathways to participation enabled by DCTs, can increase participation of patients with more diverse age, race and socioeconomic backgrounds.
Flexible experience design for both recruitment and on-study activities is essential to accessing this larger pool of patients—and retaining them once they’re on-study. Designing with flexibility in mind (e.g., offering both remote, digital visit options and in-clinic visits) is paramount because burdens related to travel, technology use, or in-home care will vary widely by persona. Many qualified patients will be excluded if protocols or study recruitment strategies are designed in an either/or manner.
Again, we can look to other industries for inspiration. For example, flexible design practices are common in product development best practices, yet they haven’t been applied to clinical trials.
To take this concept a step further, consider flipping the site/patient dynamic altogether by offering just-in-time clinical trials. This means creating a network of patients, and then opening a site near them or serving them virtually instead of the traditional route of opening a site first and hoping it can recruit patients. Turning the process on its head has many operational and regulatory hurdles, but to truly change the paradigm and expand beyond the investigator sites primarily located at large institutions, a transformational effort is likely required.
Dissolve barriers to entry with flexible design
by Sam Brown
Originally featured in STAT News
To overcome historical barriers such as lack of awareness and education, safety stigmas, or fear
of the unknown, what’s needed is a much more proactive, human-centered approach.
Effective flexible design also requires careful consideration of impacts throughout the stakeholder ecosystem. And successful human-centered design requires studying the interrelated impacts on all stakeholders: patients, investigators, site staff, and study teams from sponsors or their clinical vendors.
To take this holistic, human-centered view, it’s useful to map the experience journey of the primary stakeholder (i.e., the patient) and link it to the stakeholders and technology serving that journey. This makes the connective impact of design decisions more visible, while minimizing any unintended consequences that could benefit one stakeholder at the expense of another.
Another human-centered design principle is that people need a sense of ownership to thrive. Considering the interrelated journeys of all stakeholders is a start. However, at this time of rapid change towards future-ready, patient-centric approaches, it’s important to recognize that many stakeholders in the pharmaceutical industry have traditionally resisted change. They can’t simply be mandated to adopt or deploy new technologies and practices; they need to be educated, trained, incentivized and given an iterative feedback loop in a way that facilitates ownership in a new clinical trial.
Support the interrelated needs of stakeholders
The pandemic has advanced innovations that are changing the way clinical trials are conducted. However, to truly realize the paradigm shift towards operational innovation that’s occurring within the life sciences industry, it’s time to co-opt human-centered design best practices from other industries and apply them to recruitment and retention strategies.
The Bottom Line
Delays can cost sponsors
between and
for each day
that a trial delays a product’s development and launch.
$600,000
$8 million
Clinical trial sponsors have an opportunity
to latch onto these trends and the recent clinical trial awareness caused by the COVID-19 vaccine trials to employ similar tactics.
Increasing trial participant retention
approximately
64%
of potential patients live
more than two hours away
from existing
study centers.
70%
Clinical Trials
Disease groups are inherently heterogeneous; however,
Clinical trial populations typically are not (e.g., ~72 percent white), according to a report from the FDA.
48%
of U.S. adults have ever seen
an advertisement for one.
less than
of U.S. adults have ever
heard of a clinical trial.
50%
less than
AI has gone from an underused tool in clinical trials to one with real and valuable applications. But the transition from concepts to standards is far from complete. There’s potential for life sciences companies to do much more with AI over the next several years — representing an exciting prospect as well as a strategic challenge.
Where are we now? In Q3 2022, Point B surveyed 100 leaders who are implementing AI in their organizations. The findings confirmed our outlook: Most life sciences organizations—even early adopters of AI — still need to make a significant transition to leverage AI advances. Specifically, executives overseeing clinical trials are looking to invest in a few key areas.
Are you ready to transition to a fully digital future?
Executive Survey: The Future of AI in Clinical Trials
Insights
By Will Bryant and Jason Hirschhorn
Increasing clinical trial efficiency
approximately
61%
Improving trial design and inclusion/exclusion criteria
approximately
57%
approximately
Our study revealed that executives have three major AI
drivers on the clinical side*:
What’s driving investment in clinical trial AI?
These are challenges where the current tools and technology, standards and regulations are developed enough, and mesh closely enough, to leverage AI. Executives overwhelmingly noted that reducing the time to file and gain approval was the primary key business outcome (86%) of investing in AI. When asked what types of AI or technologies are currently being used, biometrics, machine learning, and expert systems were top of mind, but overall clinical trial functional areas are behind in investment compared to other functional areas. However, the data shows that there are aggressive plans to invest in the next 3 – 5 years.
For example, companies are using AI to accelerate patient identification and recruitment—going through health records to identify biomarker data relevant to a particular trial. AI is helping pharma and biotech companies identify the best sites for clinical trials based on past site performance and enroll patients accordingly. Big data techniques are being applied to pharmacovigilance adverse event detection. And AI is also in the early days of being applied to clinical study design. Based on performance data from past trial designs, AI can streamline trial design and protocol development.
Obstacles to broader use of AI show up in what leaders see as significant limitations*:
What are executives’ biggest challenges?
Mature AI tools and technology from retail, social media and other industries aren’t transferrable for clinical use.
Most clinical data is not yet easily accessible for AI. Studies show that between 40 – 70% of the work in data science is in accessing the data, understanding it, and cleaning it up. High-quality data is essential to using AI in such processes as evaluating the impact of inclusion and exclusion criteria on patient demographics, predicting the value of typical endpoints and identifying new ones, and exploring scenarios for complex adaptive trials.
The sexy part of AI—running a set of algorithms and coming up with actionable results—is the “easy” part. The tougher, more time-consuming work is in preparing high-quality, underlying data to run those algorithms against. And while companies are investing in digitalization now, everything up to the point of digitalization is legacy data buried in PDFs, Word documents and image files. From an API standpoint, most of the information needed to process and integrate data is locked up in historical documents.
To complicate matters, data structures and standards are highly variable across organizations—and even within organizations. Simultaneously, more data is being demanded by the FDA and EU regulatory bodies, which recognize past protocol documents as not being detailed enough to define a reproducible clinical trial. These regulatory bodies are beginning to require greater detail and, often, multiple amendments that can easily run 100 pages.
Defining common terminology and data structures can be a heavy lift, but your organization doesn’t have to go it alone. Significant progress by third-party standards organizations (CDISC, Transcelerate and Accumulus are just a few of many) will make it possible to adopt one of their versions instead of investing the time and money to do the work in-house.
Commercial tool vendors are beginning to build AI products that can be leveraged for some aspects of clinical trials, but most are essentially a combination of the same things standards organizations and life sciences companies are doing. Progress toward more mature tools has been slowed by questions of standardization and terminology. Case in point: In order to make a valuable AI tool that will fully support protocol design, it must reflect the structure of the protocol—including a controlled vocabulary. This capability has been beyond the purview of many tool vendors to date.
Diving deeper into these findings,
we see a few contributing factors:
The ability to develop and use AI in more complex clinical applications depends on three factors: new technology, industry-wide standards and evolving regulations. These three factors are progressing at different paces, making it challenging for life sciences companies to plan for the future.
Considering its current state and future potential, how does AI impact your day-to-day decisions? How will you transition from where you are to where you want to be as AI tools, standards and regulations mature over the next few years?
As you plan for the future of AI in your organization, it pays to keep several key considerations in mind:
Build-versus-buy has an explicit time dimension.
In the early days, life sciences companies had no practical options to buy AI tools. To create them, they had to hire their own data science teams, set up internal development infrastructures, and essentially do the data conversion, exploratory analysis and hypothesis testing themselves, a time consuming and costly process.
Life sciences companies are in the process of shifting their AI investments from the early days of “100% build” to a future that is closer to “100% buy.” In a well-established commercial tool environment, doing it all yourself would be like building your own ERP system — something nobody does. A “buy” approach can increase implementation speed and functionality while decreasing maintenance and acquisition costs.
Where are you today?
How you move forward will depend on where you begin. Early adopters have legacy tools and technology to deal with. Partnering to make the most of existing AI investments and determine when and where to shift from “build” to “buy” as tool vendors and standards organizations mature is critical.
If you have built AI, the questions are: What happens in the transition? How do you make a smooth, strategic shift that’s right for your company as AI tools, standards and regulations evolve?
For those just entering the AI world, you’ll want to look both inside your organization and at the vendor landscape to identify the best solutions given your risk profile. What’s your best use of AI? What skill sets and tools should be developed in-house? What should you buy?
If you are starting your clinical trial AI journey today, the questions are: Who are my best partners? How do I know they’ll address my needs? What, if anything, needs to be developed in-house?
The market shake-out will continue as technologies emerge, fade or get acquired. We expect market turbulence to continue as AI technology and standards mature. Within the next several years, we expect availability of more advanced, core functions for clinical use, such as protocol design tools
The AI trifecta will become integrated and inseparable.
The trifecta of AI influences—tools/technology, standards and regulation will mesh in ways that make it easier to place your bets and make informed, forward-looking decisions.
The future of clinical trials is fully digital.
Advances in AI technology and standards will support increasingly sophisticated tools to accelerate drug development. The power of AI will enable companies to reach more participants and increasingly diverse populations, boost participant retention, produce faster trials at lower costs, and increase reusable data. It will play a lead role in increasing the speed of getting treatments to market, giving patients access to new drugs that could change, or even save, lives.
Today’s AI is on a journey toward a fully digitalized clinical trial future—a journey your company is likely to navigate over the next few years. Now’s the time to assess where you are on the journey as well as where you want to go. Develop strategies and internal strengths accordingly. Evaluate the vendors in this fast-changing landscape and find partners that will help move your clinical trials forward. Choose the right use cases. Apply the right technology. And stay the course. The journey will be worth it.
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What's next?
How to Move Forward?
Internal
Talent
37%
Increasing Clinical
Trial Efficiency
40%
Technology Maturity
48%
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Originally published in Clinical Leader
Related Insights:
AI Readiness for the Life Sciences: The Unsexy Side of AI
Optimizing Pharma Content for Faster Product Submission
*Percentages exceed 100% because of multiple responses