As the specialty drug market has exploded in growth this past decade, patient support services programs (also known as hubs) have been forced to evolve to keep pace. For pharma and biotech manufacturers, with mammoth investments and patient lives on the line, the stakes couldn’t be higher. Maximizing the ability of patients to access and adhere to specialty medications is of critical importance to manufacturers and patients alike.
The problem is that most hubs aren’t evolving quickly enough. Many still operate with an antiquated mentality and lack of urgency, slow to adopt new tools and technologies that could dramatically accelerate the success of their pharma and biotech clients.
One of the starkest examples of this inertia can be seen when it comes to AI.
Artificial Intelligence stands as one of the most important breakthroughs for modern businesses, especially those that are consumer-facing. For much of our everyday world, including many aspects of healthcare, predictive analytics through AI is so pervasive that it’s become routine business as usual. Many sectors recognized long ago that machine learning dramatically expands knowledge of customer behavior to help companies adjust how they serve each individual.
From the customer perspective, we’ve come to accept that our personal data is used by many companies to better forecast our unique patterns and target our needs in real time. Today, we’re all used to radically customized experiences in our digital lives and beyond. We expect companies to predict our behavior and offer us the services and products that make sense for us in the moment.
AI is changing everything about how we live and how businesses operate to serve us.
But as the AI revolution rages, many patient support programs are standing on the sidelines, ignoring or just confused about this fundamental innovation. Given the vast opportunities of AI to help patient support programs anticipate and support patient needs, this blasé attitude is confounding.
What’s clear is that the head-in-the-sand act can’t persist much longer. The only question becomes: when will manufactures start demanding that their patient support programs integrate AI to accelerate the success of their specialty medications?
Giant market, giant untapped opportunity
We’re long past the point when hubs served as basic call centers for a small number of complex medications. Leading-edge patient support services have become essential to the success of all specialty medications, providing critical wraparound support for an increasingly competitive and challenging marketplace.
The U.S. specialty drug market reached $200 billion in 2017 and is expected to grow to $500 billion in 2020. When combined with orphan drugs, spending on specialty pharmaceuticals comes close to equaling the amount spent on traditional medications in this country.
Patient support service programs are the conduit that helps patients initiate and maintain a connection to this mammoth marketplace. As they do so, hubs have access to wide swaths of patient data that comprise a goldmine of opportunity for personalization and customized interventions.
If patient support programs want to plumb this goldmine that they’re sitting on, AI is the pickaxe they need.
Sure, some patient support programs are currently creating smart data models, defining patient types, and building robust spreadsheets around patient segmentations. Some of these programs seek to build complete psychological profiles of patients as part of their risk stratification strategy, which guides their approach to issues like access and adherence.
But that’s merely skimming the surface of what’s possible today.
Machine learning can extract the true value of patient support services data. This includes strengthening patient support programs, helping employees get smarter through keener insights about patients, improving customer relationships, and guiding support programs around precise resource allocation so that the patients most at risk receive the most attention.
This is exactly the sort of increased specialization and flexibility which many analysts agree that patient support service programs need as manufacturers develop ever more complex drug products.
Demanding modernization from patient support programs
So what’s the difference in practice between AI and the kind of sophisticated data modeling currently in use by many patient support programs?
While many hubs develop profiles of patient groups to help drive resource expenditures, AI allows patient services to significantly sharpen the picture of each patient and thus finetune how they predict challenges and tailor responses for every consumer.
Real world data collected from many points along a patient’s journey is far more instructive than the helpful but more generic patient profiles developed by most hubs today. Basic AI solutions can generate associative memory and predictive modeling that connect disparate data elements which are too complex to show up on typical spreadsheets. It’s the difference between segmenting groups of patients based on how you think they will behave and micro-targeting actual patients based on individual data in real time.
Patient support services have long understood that they can increase patient success when programs are better informed about how to dedicate their time and capital, earmarking more to patients who need the most support. With AI, hubs have more bandwidth to focus on the highest risk patients. That’s because they’re less distracted by patients who may seem to fall within a high-risk profile but – with the pinpoint insights gained by AI – are actually low risk.
Today, pharma and biotech manufacturers are racing for the future of patient support, vying for ever more sophisticated patient insights to develop laser-targeted interventions. As manufacturers develop more advanced medications for chronic and complex disease states, they must expect a new level of service from patient support providers.
Whether they want to or not, patient support programs will eventually be forced to evolve with the specialty medication market. AI is an essential step in this evolution. Just as other industries have come to rely on the deep customer insights enabled by AI, pharma and biotech manufacturers must insist that patient support service hubs integrate AI within their programs as a matter of common practice.
Brian Hare is Head of Business Development for AppianRx, a healthcare technology firm that uses AI solutions to help companies solve their specialty medication challenges.