“AI is fascinating. But how does it help me solve the problems of my specialty medication company?”
It’s a question I hear a lot.
I lead business development for AppianRX, a healthcare technology firm that uses AI solutions to help manufacturers increase patient adherence and decrease abandonment with their specialty medications. In the course of meeting with manufacturers and patient support services leaders around the country, I have found that there’s plenty of awareness of AI in general but less understanding for how machine learning and other tools translates into specific bottom line advantages.
Part of the challenge in explaining the value proposition of AI is this: every company is unique. When it comes to patient support service programs, specialty medication manufacturers all face different landscapes of opportunity and difficulties with keeping patients adherent to their drug protocols. AI will therefore serve each business in unique ways that are particular to a company’s specific situation.
AI tools work by first ingesting the myriad data points that companies may not even be capturing themselves about each patient, and then creating a model that identifies key issues. With deep intelligence about the historical patterns of their programs and patients, companies can more precisely allocate resources, develop laser-targeted interventions, and improve the success of their medications.
So to answer the question: “How does AI solve the problems of my specialty medication company?” The problems that AI solves for your specialty medications can’t be answered until you fully understand your problems.
And you won’t understand these problems until you’ve engaged with AI.
AI is changing everything about biotech and pharma
AI is no longer a shiny new idea within pharma and biotech. It’s an established technology that has been transforming the entire healthcare landscape for years.
A CB Insights report found that approximately 86% of healthcare organizations, life science companies and med tech firms were using artificial intelligence technology in 2016. According to a study by TechEmergence, more than 50% of healthcare industry executives currently using AI anticipate broad-scale adoption of AI by 2025. Big pharma names announcing deals and applications related to AI include Bayer, J&J, Merck, Sanofi, Genentech, and Pfizer.
As AI is used for drug discovery, robotic surgery, diagnosis of certain conditions, and myriad other medical innovations, the advances have prompted the development of an industry group called the Alliance for Artificial Intelligence in Healthcare (AAIH). Formed by BenevolentAI, GE Healthcare and Insilico Medicine, amongst others, AAIH advocates for public policy, regulation, and market access for AI-developed products.
Pharma and biotech leaders know that AI can deepen insights, improve operations, and dramatically scale capabilities, but they still struggle to implement AI and machine learning across their businesses. As noted in PharmaTimes, a Harvard Business Review found that only 8% of chief executives successfully led enterprise-wide AI initiatives, indicating the pace of adoption is slower than the speed of AI/ML advancement.
One particular area of pharma and biotech where AI adoption is lagging is in patient support services.
Other industries are decisively stepping up to integrate AI technologies into their contact centers and customer service operations. It’s an obvious area of excitement, as improving efficiencies in customer service increases customer loyalty and brand retention and allows employees to focus on other areas that provide greater returns.
But specialty medication manufacturers are suffering from inertia when it comes to modernizing their patient support services.
Real world data creates real world solutions
Perhaps as pharma and biotech leaders see the tangible results of AI on their patient support services, they’ll hasten the adoption of AI initiatives.
Let’s consider one fictional scenario that illustrates the potential benefits of AI for patient support programs.
FictionalPharma manufactures a complex medication for kids. The company faces numerous challenges with increasing access and identifying potential non-adherence. This includes a complex enrollment form to meet HIPAA compliance; the need to send free month-long starter kits before authorization; and heavy resource allocation to train caretakers how to use medication.
The company collects plenty of data about every patient. But raw data alone doesn’t offer insights. It’s difficult for the humans leading patient support to connect the dots and understand which events are triggering what actions related to adherence and abandonment.
How can FictionalPharma move away from a one-size fits all approach? How can they customize interventions and connect that activity to proven outcomes? How can they optimize programs based on past knowledge learning and apply it to ongoing patient/ caretaker outreach in real time?
What FictionalPharma needs is a usable, action-driven model that guides the company on how, where, and when to deploy resources.
AI can automatically create this sort of model in ways that are impossible for humans to replicate at scale. An AI-driven model would create specific risk scores for each patient and give FictionalPharma the information they need to operationalize patient insights.
With this intelligence in hand, FictionalPharma can make smart decisions that save money, time, and resources. For example:
- Predictive modeling regarding authorization timing can guide the company on how much free starter medication each patient should receive.
- The company can determine which patients are at high risk of not refilling their medication and create tailored interventions to keep those patients adherent.
- The company can identify which patients are low-risk and don’t need the costly, high-touch support of nurse educators.
- The company can better understand when to start, stop, or continue interventions for each patient.
As someone who built an early days hub when patient support service programs were just getting started, I can tell you that I would have jumped at the opportunity to integrate AI into our platform. Back in the day, we were throwing darts at a dartboard, guessing about what actions to take to keep our patients adherent.
Not much has changed in the past decade. Most patient support service programs are still playing darts, and the success of specialty medications is suffering as a result. AI changes that equation and allows programs to take intelligent actions based on real world, real time information that can translate into bottom line impact.
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.