Advances in personalized medicine over the last decade have made substantial changes to oncologists’ treatment approaches across most oncology disease states with notable benefits for patients. However, with this progress comes a number of challenges, including more complexity in recruiting for clinical trials. This is because more personalized therapies often equate to more restrictive eligibility criteria, making it difficult to identify and successfully enroll qualified patients. With an estimated <5% of adult patients with cancer enrolling in clinical trials, it is unsurprising that ASCO has multiple sessions this year focused on how to improve enrollment (Biden Cancer Initiative Colloquium, Overcoming Barriers to Clinical Trial Enrollment).

At W2O, our solution is to partner with clients to harness the power of data science, accelerating clinical trial recruitment through a number of data-driven advanced targeting optimizations.

1.PUBLISHER/SEM OPTIMIZATION

We leverage data from the digital landscape to determine which advertising publishers are most relevant and valuable to patients. We analyze what websites patients most often reference in their online conversations and search activity related to their disease, treatment, general health, and broader non-health topics, giving us insight into where to place clinical trial ads for optimum reach. We also optimize paid search ad copy and keyword strategy for clinical trial enrollment by incorporating the actual language of the patient, garnered from linguistic analysis of real patient dialogue online, as well as the terminology actual patients use when searching for information.

2. LANGUAGE MODELING

We also use AI to model patient language so eligible patients navigate to the right clinical trial faster. Employing established language modeling techniques, we estimate whether an individual account can be classified as a patient based on the way they write. We tap into our audience first platform, mDigitalLife, to validate language patterns of known patient usage on social media, forums, and blogs, ensuring the model’s accuracy in identifying eligible patients. Upon discovery and validation, we can then target identified potential patients directly using social advertising tailored to their online profile.

3. LOOKALIKE MODELING

Another mechanism for identifying potential clinical trial participants is to personalize targeting based on brand, activity, and content affinities. We develop a normative audience of known patients to determine audience interests and behaviors based on publicly available data. These insights feed the algorithm we use to model other probable patients across social networks. From there, groups of people whose collections of interests match that of verified patient populations are targeted with messaging about clinical trials for which they are eligible.

4. INFLUENCER ACTIVATION

The last optimized targeting approach we will highlight is discovering and activating online influencers to engage patients in search of a clinical trial. This begins by completing an influencer analysis to determine who can most effectively engage potential patients for a particular disease state/clinical trial based on a complex set of metrics. This analysis offers a detailed view into who drives decision-making and behavior shifts – and thus, who patients look to for advice (directly or indirectly) when considering their next treatment choice. The most authentic, engaging partnerships between influencers and companies are produced when both parties have mutual priorities and interests. Clinical trial opportunities in the oncology space are a frequent topic that influencers discuss and disseminate, making it an optimal engagement area for pharmaceutical companies looking to efficiently extend the reach of clinical trial enrollment messaging.

Using digital technology and innovative, data-driven techniques can increase speed in finding patients for the highly targeted and specialized trials common in today’s oncology landscape. The good news is that data is readily available to efficiently inform these decisions – we just have to listen to what patients are telling us through their online behaviors.

Stay tuned for more 2019 ASCO content! Check Ujwal Pyati’s perspective on community oncology.


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