How to Improve Recruitment Rates, Reduce Site Burden, and Avoid Wasted Spending

Matt Walz

When Facebook banned ads that target patients based on “sensitive data” related to their health condition, it might have been easy to conclude that the social media giant would stifle drug development research as a result. While Facebook is just one example of social media used by clinical trials to recruit patients, we would estimate that Facebook accounts for roughly half of all direct-to-patient recruited participants for a given trial, based on our observations from running global patient recruitment campaigns in the past few years.

The truth, however, is that Facebook’s ban, which went into effect in January, is not a setback.

It’s an opportunity.

The ban should push clinical research to accelerate more patient-centric advertising models that enable privacy while ideally improving public health by increasing access to clinical trials.

Digital advertising for clinical trials should be about getting potentially life-saving drugs to market more effectively, not broadly targeting anyone who’s visited a cancer charity website for an oncology study. This kind of wide-net casting is not nearly as effective as most believe. The future of healthcare and clinical research advertising depends on our ability to create greater impact by analyzing underlying data. The way to get there is through patient-centric advertising models.

Traditional digital advertising challenges

For decades, digital advertising has been part of clinical researchers’ playbooks to supplement the pool of patients available to investigators. However, these traditional approaches have been far from successful. They often result in extensive, burdensome screening activities only for the sites to enroll a few more patients.

More traditional interest advertising (based on interest in products or topics) and predatory advertising models are much more likely to lead to a poor outcome for underrepresented groups of patients, especially when clinical researchers rely solely on Facebook’s native targeting tools.

Shifting gears is not so easy. It’s a new frontier for data privacy, especially as high-profile breaches dominate cybersecurity news. Gone are the days of the third-party cookie. In 2022, addressing privacy concerns is a must, and that is where the Facebook ad ban is rooted.

Many consumer brands are taking steps to make consumers more comfortable about their privacy during online activities. When it comes to clinical research, the end goal is to inform and benefit as many patients as possible, not sell.

Many digital recruitment companies are adjusting. Despite how specific an advertisement may seem, digital recruitment companies do not need to know any personal patient information when they are seeking to draw in potentially eligible candidates. In fact, no personal information is known until the patients themselves opt into the enrollment process.

Ultimately, this kind of patient-centric advertising calls for a more personalized experience for users by explaining why particular ads were directed toward them. In a world in which the internet is more and more dominant in our daily lives, it’s essential to continue using it as a tool to drive improved success rates of clinical trials.

Better targeting through smart modeling

Thanks to the acceleration of data and cloud adoption, those in charge of trial recruitment and enrollment can employ tools and solutions that allow them to better target audiences more likely to fit their studies’ eligibility criteria.

Newer sophisticated digital marketing approaches utilize different platforms such as Google, Amazon, Taboola, and others to push ads to thousands of other web pages and millions of people, then take note of respondents and utilize the resulting information as a learning loop into audience modeling. It’s also possible to feed these learnings into Facebook’s modeling, which boosts advertising effectiveness on the platform. Qualified patients can then access clinical recruitment information without having their sensitive personal data misappropriated to target them.

Patient-centric, smart modeling techniques expand the pool of potential clinical trial participants and then narrows down the pool to only the most relevant candidates matching the study criteria. These models start with initial patient personas, developed from large RWD repositories, and then continuously refine themselves based on engagement. This adaptive, hyper targeting approach provides sites with a steady flow of motivated and study-ready patients and allows them to focus on the final clinical evaluation prior to enrollment.

This is not the time to lament Facebook’s decision. Instead, the ad ban represents an opportunity to begin a conversation between the pharma industry and social media channels. RWD and analytics provide solutions for some of the most long-standing barriers to clinical research studies, but must be truly patient-centric and used responsibly.

Matt Walz, CEO, Trialbee