By: Katie Carr, EVP, Chief Revenue Officer
Using predictive technology, healthcare marketers can target patients before they are diagnosed, progress to a new treatment or become non-adherent – and more. Here’s how to implement predictive AI to usher in a new era of advanced targeting.
Instead of exclusively relying on past events to target audiences, life sciences advertisers can now proactively engage the right patients and providers at key decision points in the diagnosis and treatment journey using predictive AI. By analyzing vast amounts of data, this technology can forecast future health events, treatment progressions, patient adherence patterns and more.
Leveraging predictive AI, marketers can engage with their ideal target audience more effectively, getting ahead of customers and even influencing their behavior at critical moments in the health journey.
Targeting Based on Future Events
Predictive AI allows marketers to go beyond understanding patient needs based on past events and instead anticipate them. By uncovering individuals who show early signs of a medical condition, predictive AI serves as a critical tool for facilitating earlier diagnoses and accelerating time to treatment. Being able to pinpoint thousands of patients at risk of developing a condition like rheumatoid arthritis well before clinical signs manifest enables earlier intervention, potentially improving outcomes.
Beyond early diagnosis, predictive AI is instrumental in streamlining the launch of new pharmaceutical products. By analyzing data to predict which healthcare providers are most likely to embrace novel treatments, pharmaceutical companies can target more strategically early in a launch. This focused approach ensures that information about new medications reaches those who are inclined to adopt them more rapidly than others, accelerating conversion and maximizing the launch’s impact. Then, later in the commercial lifecycle, that prioritization can switch, focusing on HCPs who are more likely to adopt a product after being proven in the market. The result: optimized budgets targeting those most likely to adopt a therapy during specific periods in time.
The technology can also be utilized in refining insurance formulary targeting. With predictive AI, it becomes possible to accurately determine which patients are likely to have the ideal insurance coverage for certain treatments. This insight is invaluable, allowing for more precise marketing campaigns that connect eligible patients with the therapies they need, without the barrier of coverage uncertainty.
Another significant challenge in healthcare is medication adherence. Predictive AI can identify patients who are at risk of not following their prescribed medication regimens, which is a $500 billion cost to the industry every year. With knowledge of what cohort is most likely to become non-adherent within the next 30 days, marketers can design specific campaigns to engage and educate these patients, mitigating the risk of adverse health outcomes and the associated financial burdens on the healthcare system.
Predictive AI can also play an essential role in monitoring and managing treatment progression. By recognizing patients whose conditions are likely to worsen or aren’t responding to their current therapy, brands can proactively target messaging that educates patients and their physicians before a next-line treatment decision is made. This is especially crucial for conditions where early modification of a therapy can lead to better management of the disease and enhanced quality of life.
The Implications of Predictive Audience Targeting
What does this mean for healthcare marketing? Advertisers can now identify and target individuals who are at risk of developing a condition, those likely to transition to a new treatment, or patients at risk of becoming non-adherent to medication regimens, as well as target based on insurance coverage and get ahead of treatment progression. This opens up the door to a much more sophisticated and comprehensive approach to targeting. Marketers can use predictive models to manage marketing resources more efficiently, enhancing engagement and education to optimize marketing budgets and increase revenue. Predictive AI can lead to higher conversion rates, as relevant ads are more likely to resonate with patients and HCPs, enabling brands to engage audiences at critical junctures in the diagnosis and treatment journey, from pre-diagnosis to treatment selection and start to living with the condition.
Predictive AI In Action: Real World Use Cases
Swoop is the first in the market to develop predictive targeting based on what will likely occur - rather than what already has - that marketers can leverage to reach and interact with the right audiences proactively at pivotal moments.
In the case of undiagnosed patients, Swoop’s predictive capabilities have been particularly impactful. For instance, in a group of 5 million individuals, Swoop’s AI tools uncovered 42,000 de-identified patients who were likely to be diagnosed with rheumatoid arthritis. This prediction was made a full six months in advance of the actual diagnosis. The significance of this early identification cannot be overstated, as it enables preemptive patient education and early treatment interventions, with the potential to drive increases in prescription rates between 10-16% and overall patient lifetime value.
The benefits of predictive AI extend to the critical product launch period. For instance, during the introduction of a new migraine medication to the market, Swoop identified a group of healthcare providers who had a history of adopting new treatments at twice the standard rate. This information would allow a pharmaceutical brand to focus its marketing efforts more effectively at launch, leading to a more successful adoption rate, followed by a reallocation of funds later in commercialization towards physicians who typically take longer to adopt new therapies.
Addressing the complexities of insurance coverage, Swoop’s predictive AI uncovered 4 million patients out of 16 million who were potential migraine sufferers and most likely to have insurance coverage for a leading therapy. By targeting these patients with a tailored marketing strategy to reach those with a higher probability of accessing treatment, a pharma brand could see a 3-4 fold increase in marketing efficiency.
Another significant challenge in healthcare is patient adherence to medication regimens. Swoop’s AI technology successfully predicted which patients were at risk of non-adherence to their diabetes medication within the next month with 94% accuracy. Similarly, Swoop accurately predicted 92% of all patients who became non-adherent in the next 30 days for a multiple sclerosis treatment and 91% for a depression therapy.
These examples underscore the power of Swoop's predictive AI in creating actionable insights that not only benefit patient health but also optimize the performance of pharmaceutical campaigns. Ultimately, predictive AI represents a seismic shift in how healthcare marketing is conducted. By pivoting from a reactive to a predictive approach, advertisers can target patients more effectively, deepening their education and connection to a brand.
EVP, Chief Revenue Officer
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Katie Carr brings more than 20 years of client-facing experience to her role managing a team of professionals focused on helping pharmaceutical and life sciences brands leverage custom direct-to-consumer and healthcare provider audiences to improve engagement, drive Rx lift and optimize patient outcomes. Before joining Swoop, Katie held roles at Google and Microsoft where she was responsible for driving significant revenue growth for their advertising portfolios. |