Does Your Patient Support Program Really Know Your Patients?

Patient support contact center

As the specialty drug market has exploded in growth this past decade, patient support service programs 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 stagnation 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, machine learning is so pervasive that it’s become routine business as usual. Many sectors recognized long ago that AI 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.  

Machine learning has upended 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, overwhelmed by, or just confused about this fundamental innovation. Given the vast opportunities for how predictive algorithms can help patient support programs anticipate and support patient needs, this rush to inaction is confounding.  

What’s clear is that the head-in-the-sand act can’t persist. It begs the question: when will manufacturers start demanding that patient support programs integrate AI to maximize the value of their specialty medications for every patient?  

Extracting and using valuable patient data    

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 increasingly complex and challenging patient populations. 

Hubs have access to robust patient data that can enable rich opportunities for personalization and customized interventions. But without machine learning technologies, it’s impossible for programs to extract this data at scale and use it to tailor patient support around medication access, adherence, and abandonment. 

Sure, some patient support programs are doing their best to create data models, define patient types, and build spreadsheets around patient segmentations. Certain programs build psychological profiles of patients as part of their risk stratification strategy, which guide their approach to increasing patient adherence. 

Also read: Patient Adherence Shouldn't be a Guessing Game

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 service programs around precise resource allocation so that the patients who need the most attention can receive the appropriate level of support.  

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.  

Photo by Petr Macháček on UnsplashPhoto by Petr Macháček on Unsplash

Demanding modernization from patient support programs  

So what’s the difference in practice between AI and the kind of data modeling currently in use by many patient support programs?  

Patient support services 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. While some hubs try to develop basic profiles of patient groups to help drive resource allocation, 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 some 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.  

With machine learning, 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. 

Predictive models help companies accurately identify which patients are at risk of non-adherence and why. Armed with these insights, patient support programs can become smarter about getting to the right outcome for their patients and increase the number of patients who will be compliant. And, just as important, predictive modeling helps companies understand which patients have risk profiles that programs aren’t capable of addressing. Sophisticated algorithms help companies optimize resources where they will have the most impact.   

Pharma and biotech manufacturers should be racing for the future of patient support, vying for ever more detailed 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 patient-specific support from service programs.  

AI is an essential step in this evolution. Just as other industries have come to rely on the deep customer insights enabled by predictive analytics, pharma and biotech manufacturers must insist that patient support service hubs integrate machine learning within their programs as a matter of common practice.

Also read: AI is Raising Expectations for Your Contact Center

Author: Brian Hare

Brian Hare is Head of Business Development for AppianRx, an AI-focused technology firm building innovative products to solve the complex business problems of healthcare.