Mike Rousselle, OptimizeRx

Pharmaceutical brands have successfully deployed artificial intelligence (AI)-driven real-world data (RWD) programs at the point-of-care, providing both access and cost savings for lifesaving therapies. Life sciences can now identify which physicians are treating patients for rare diseases and those who have patient populations in need of financial assistance, and who are able to deploy technology to help them access therapies and assist with unexpected costs. What’s more, by applying AI to RWD sources and messaging at the point-of-care, pharma brands can anticipate when patients will visit a healthcare provider and reach those providers at a time when they need the relevant brand or financial assistance information the most.

Given recent successes with the application of AI to RWD for life sciences, a robust AI/RWD capability will become table stakes for any pharmaceutical marketing program in 2023. Just a few years ago, applying AI to data held potential for life sciences to reach their target patients, but it was not proven. Predictive solutions have since demonstrated that they can not only accurately identify when providers are treating patients of a certain profile, but that this information can then be leveraged to support better patient outcomes. Now, life sciences companies are successfully using AI technology for provider programs at the point-of-care, making this technology not just a “nice to have” but a “must have” for any pharma brand program. Not only do brands want to be first-to-market with innovative technology solutions, but more importantly, these solutions are helping to support both providers and patients with access to therapies.

An AI-enabled program at point-of-care meets a variety of challenges that pharma brands typically face. The first advantage of applying AI to RWD is that the programs through which these insights are deployed (e.g., point-of-care messaging) will naturally improve over time as the algorithm learns. By its very nature, an AI model is just a series of experiments that are designed to learn every discernible pattern from the data it's being fed. And given the vast longitudinal data that each patient has in a real-world dataset (claims, labs, etc.), an AI model will almost always be able to perceive patterns and deliver better insights than more traditional RWD analytics. Further, as more brands deploy AI programs, the cumulative learnings of the system will begin to compound, and all brands will be able to hyper-target their messages to the physicians and patients of interest. Even after a program is launched, the data and AI model can be evaluated and adjusted in an ongoing collaborative process between brand and vendor, and the entire system will continually improve. This AI development lifecycle enables scale across programs but achieves a level of fine-grain customization to allow each brand to be hyper-specific in its patient identification and message timing.

Another benefit is precision. For the first time, brands are using this application of AI technology to identify a highly targeted audience of patients and their providers. For example, it has been successfully used to identify a small subset of patients that failed a first- or second-line drug, to anticipate patients that will be eligible for a third-line therapy. This technology also incorporates proprietary data from point-of-care messaging platforms, knowing when a brand’s messaging will not only be timely and relevant for a patient, but also which providers are most likely to utilize that information within an electronic health record (EHR).

It has also been successfully used to identify patients before they enter a health insurance coverage gap, to prevent discontinuation of medication, and keep patients on their preferred therapy for longer. In summation, using AI helps brands identify the right providers and address messaging issues, even before a problem arises.

AI applied to RWD also has a high degree of accuracy in targeting patients who are newly diagnosed, misdiagnosed, or who will become eligible for more advanced therapies. Since the technology is receiving and using new data much faster than other marketing and engagement methods, it can find newly eligible patients quickly, and adjust as underlying prescribing patterns change. This agility is extremely important in healthcare, where it can mean access to lifesaving therapies for patients who might otherwise have been left wanting. AI-enabled technology is game-changing in its ability for pharma brands to reach new or existing patients who can benefit from their treatment, right at the time when they enter eligibility, and patients who may have an extremely narrow window of eligibility for a specialty drug.

AI-driven technology is customizable for brands and patient populations. AI allows us to look at and consider various questions, such as, for example, “what is your patient journey like?” “And where are you not getting enough patients or the ones you want?” Further, it allows for hyper-targeting of providers and time windows, and readjusting on the fly as healthcare circumstances change. Marketers include this technology in the marketing mix not only because it’s a differentiator, but because it can be tailored exactly to the brand and challenges they need to solve for.

An oncology brand came to my team with several brand challenges: how to identify a specific group of patients who would be likely to fail their first two lines of cancer treatments, and, therefore, likely be candidates for this product, a third-line treatment. Even further—beyond identifying who the right patients were, the brand also needed to reach those patients at the critical moment when they’d be transitioning from their second to third line of therapy. And once there—with the right patient at the right time—identify the proper healthcare provider (HCP), among the patient’s care team, who would most likely be able to see and take action on the messaging they saw within their EHR.

This AI/RWD technology enabled precision messaging directly to the target audience of providers, right at the point-of-care, within a week of the likely patient-provider interaction. This system pioneered a way to find and reach this mercurial patient population and significantly impact quality of life for patients. The program prompted oncologists to consider and initiate third-line therapy, and resulted in increased patient starts on the therapy, and ultimately, a meaningful improvement in patient outcomes. Specifically, within a 12-month program, patients starting this therapy increased by 28%, and new patients from the targeted HCPs grew by 33%.

Another life sciences brand was challenged with patients that were discontinuing medication or switching to a lower-cost brand due to the Medicare coverage gap, often referred to as the “donut hole.” A predictive data program using AI/RWD solved this issue by notifying HCPs 

 their patients entered a Medicare coverage gap, so that providers could 

help patients anticipate any unexpected costs and enroll in the manufacturer’s cost-support program. This novel marketing strategy allowed life sciences organizations to reach physicians at the point-of-care through EHR messaging and provide timely solutions, including financial assistance to patients right at the critical moments before they were at risk of discontinuing a drug due to higher out-of-pocket costs. Physician communications at the point-of-care helped encourage enrollment in the manufacturer’s cost-support program, keeping more patients on the therapy and increasing adherence at large. Results over a six-month period for a life sciences brand include:

46,000+ incremental scripts among HCPs receiving affordability information and financial resources for
their patients.

27% of the HCPs identified and targeted for the affordability information program enrolled a patient for the first time.

200% growth over the manufacturer’s initial number of HCPs identified with “at-risk” patients.

Predictive analytics results in innovation

In general, leveraging any marketing strategy with predictive analytics is innovative for the life sciences industry, because it can be applied in real-time to anticipate and identify events in patient journeys that represent optimal opportunities for messaging. AI/RWD as a combination ensures that each message sent has the highest possible likelihood of making an impact on physician mindshare, and on patients’ lives. This solution can help brands preempt and message against upcoming access issues across different therapeutic areas, so that providers can help their patients continue to access their prescriptions.

And it’s proven highly successful, both for life sciences brand teams communicating about niche therapies with smaller patient audiences, as well as brands targeting larger patient populations.

As long as there is a critical moment in a brand’s patient journey—a point at which brand messaging would have an outsized impact—AI/RWD will help a brand find as many of those high-leverage messaging opportunities as possible. Given the adaptability of AI models, the same technology can be applied to many brand challenges and hard-to-reach populations, making this marketing strategy highly impactful for the life sciences industry in the year ahead.

Mike Rousselle is VP of Data Product at OptimizeRx.

Articles

How artificial intelligence and real-world data has not just helped predict patient behavior, but has also been successful at the point-of-care for life sciences companies.  

Applying AI Case Studies in Pharma 

The application of real-world data (RWD) for pharmaceutical brand marketing has evolved from the traditional model of executing on historical data, to AI-driven strategies that allow brand teams to execute on evidence-based insights, drawn from real-time data powered by predictive analytics at the point of care (POC). This gives pharma brand teams the ability to proactively alert physicians about relevant treatment information based on dynamic data about their patient’s journey.

As the healthcare industry continues its accelerated adoption of digital solutions —largely spurred on by the Covid-19 pandemic—improving patient care and adherence through technology must start with optimized insights at the POC. Reimagining RWD from how it’s been traditionally leveraged by pharma is key.

There is a vast opportunity for life sciences companies to use AI-driven solutions to bridge the gap from data to insights to execution, bringing everything together into one seamless closed loop. The application of predictive analytics, using machine learning (ML) methods applied to RWD, can speed time-to-therapy, and support positive outcomes by enabling life sciences to help healthcare professionals (HCPs) identify patients who may be qualified for specific therapies, by raising awareness of qualification parameters and patient access pathways, as well as identifying early indicators of non-adherence among patient populations in real-time. AI-driven solutions also enable personalized HCP engagement programs based on up-to-date demographics, disease and care milestones of their specific patient panels.

This kind of technology advances POC communication by layering AI-driven algorithms on top of real-world datasets to solve high-impact awareness, access and adherence challenges, optimizing the feedback loop between life sciences and providers, ultimately supporting better patient outcomes.


Digitizing access is the gateway to adherence

The quest to solve drug awareness, access and adherence challenges is less elusive now than it’s ever been, thanks to technology solutions at the POC. What’s new is how the life sciences industry and its partners are applying new technologies to proactively support and engage patients.

AI-driven predictive analytics can help pharma brands execute more intelligent digital commercialization strategies to simplify therapy initiation by presenting HCPs with a fully electronic option for enrollment, benefits verification, prior authorization and patient support onboarding. This proactive approach enables pharma manufacturers to support patients by removing an obstacle in their journey and helping them get started and stay on their doctor’s recommended course of therapy.

One example of how AI-driven RWD marketing tools at POC helps patients obtain the therapies they need is in the area of specialty medications. The increasing availability and use of these therapies has exposed unique barriers to prescription fulfillment processes and patient access. This has created complicated healthcare delivery workflows resulting in hurdles that impact a provider’s ability to optimally prescribe, and for patients to receive specific therapy. A recent physician survey by the American Medical Association

 found that prior authorization process delays have a significant effect on patient outcomes, with 90% of physicians surveyed reporting that the prior authorization process has somewhat or significant negative impact on clinical outcomes, and 30% reporting that this process has led to a serious adverse event for a patient in their care.

For conditions requiring specialty medication, AI-based solutions can reduce the provider burden and improve patient care. Predictive analytics simplifies the prescribing process and helps patients access the specialty drugs they need by addressing the enrollment and approval of specialty drugs at the earliest possible point in the prescribing process. It also ensures that key enrollment and affordability information is delivered based on the disease state or benefits profiles of each provider’s patient population in real-time.

A novel approach to identify real-time needs

For years, the life sciences industry has been executing on RWD insights months after the data is collected—in a retrospective, rather than prospective needs-based manner. Typically, RWD and predictive analytics are leveraged by pharma marketers to predict the influence of a physician on prescribing trends and anticipate future prescribing needs and volumes based on past physician behavior. Using this model, life sciences sales and marketing teams determine which HCPs may need what information about their treatments.

Evidence-based physician engagement uses a novel approach to understanding which HCPs need specific information, because it doesn’t rely on past physician behavior to predict future need. Instead, it uses patient characteristics like disease progression and coverage information to identify current needs among the patient populations being seen by specific HCPs. This can be seamlessly integrated at POC, including in the electronic health record (EHR).

The benefits of real-time, evidence-based engagement include:

Bridge the months-long time gap that manufacturers have historically faced between when data sets are created, and when they can execute on that data.

Enable pharma brand teams to employ a predictive, proactive approach to execution on data insights at POC, bringing it all into a seamless closed loop.

Facilitate contextual engagement between manufacturers and HCPs at the POC, which can speed patient access to therapy and support adherence that can have a positive impact on outcomes.

To truly capitalize on this untapped value in the short run, life sciences companies need to focus on identifying steel thread use-cases where value can be returned quickly on these projects. One real-world evidence (RWE) use case that is currently being utilized by a top pharma manufacturer is to provide visibility to doctors when Medicare patients’ treatment plans are at risk of lapsing due to loss of coverage. An AI-driven RWD engine is helping the manufacturer determine when to notify physicians that patients in their panel may be eligible for financial assistance. This helps ensure that patients who qualify for therapy can continue to follow their physician’s preferred treatment plan without interruption, due to new and unexpected out-of-pocket costs.

Leveraging AI-driven data throughout the patient care journey

The biggest untapped opportunity is the use of RWD and AI to reach providers and patients with more timely and relevant information at critical junctures throughout the patient care journey, at the POC and beyond. AI-directed, real-time HCP marketing raises awareness of treatment benefits to give patients a timely start on therapy while personalized digital patient support programs help patients stay on therapy. This allows the pharma industry to close the loop and interact with HCPs and patients by enabling care-focused engagement throughout the patient care journey. The ability to leverage actionable, AI-driven, integrated data at the POC and beyond is key to unlocking streamlined communication and processes around timely therapy initiation to reduce abandonment, and design personalized, successful adherence programs to help patients stay on their doctor-recommended course of therapy.

The future of AI-driven solutions for life sciences

As the healthcare industry continues trending toward interoperability in the coming years, being able to message HCPs and patients at various points throughout the care journey will become the standard. HCPs and patients will be inundated with messaging, and the value-driving lever will move from message volume to message quality—measured by relevance and specificity. That’s where RWD and AI will play a key role by enabling precision in messaging—identifying smaller subsets of HCPs and patients that are exactly the right audience for each brand, at exactly the right time for that message to affect behavior—supporting timely therapy initiation and driving adherence.

The real value brands can add to the patient’s care journey lies not just in knowing which HCPs to message, but also how those HCPs operate in their EHRs and when those physicians are seeing patients who qualify for therapy. AI will be used to glean insights beyond those visible solely in claims data. It will be combined with additional sources of data to compensate for the gaps in conventional RWD—overcoming the time-lag in most RWD datasets, and identifying patients who are eligible for therapy changes even before they show that tendency in their claims history.

In short, AI-driven solutions will change the way we think about the entire value proposition of life sciences messaging.

Mike Rousselle is Vice President of Data Product, OptimizeRx.

https://www.ama-assn.org/system/files/2021-04/prior-authorization-survey.pdf

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