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AI-driven strategies for pharma brand marketing teams to help bridge the gap from data intelligence to execution
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 Association1 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:
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.
About the Author
Mike Rousselle is Vice President of Data Product, OptimizeRx.