Pharma Is Reclaiming Control Over Patient Experience. What Does It Mean For The Hub Model?

When patients are prescribed complex therapies — like specialty drugs, cell and gene treatments, or high-cost biologics — the path to treatment is rarely straightforward. 

Filling a prescription can involve a dizzying array of specialty pharmacies, as well as third-party administrators, insurance hurdles, and piles of paperwork. Each step in the process is a potential point of failure.  

All too often, that ends up being the case. In fact, nearly one in 10 prescriptions are abandoned, and that portion rises to 60% with more expensive medications and out-of-pocket costs of more than $500. Patients might receive guidance along the way, but it’s often scattered, inconsistent, and hard to navigate when they need it most.

We need to make the entire process easier — and we’re better positioned than ever to do it. Advanced technologies are giving pharma companies the compliant infrastructure needed to regain visibility and control over the patient experience. 

For the first time, manufacturers can see where patients are encountering barriers in real time — without compromising privacy — and act before patients walk away.

How services hubs are evolving for the patient-first era

Pharmaceutical companies have long relied on the “hub” model to support patients as they start, or continue with, drug therapy. This includes outsourcing patient services to third-party call centers designed to assist patients in accessing physician-prescribed medicines, overcoming insurance barriers, and enrolling in financial assistance programs. 

When it comes time to fill a prescription, patients face a cascade of administrative burdens: insurance approvals, denied coverage, copay shock, paperwork cycles, enrollment delays, and more. For patients prescribed high-cost or specialty therapies, a positive or negative interaction with a pharmaceutical company can determine whether a patient follows through with their treatment plan. 

For years, this setup delivered scale and specialized knowledge that helped pharma manage patient needs across a fragmented healthcare landscape. But as therapies grow more complex and patients expect a higher standard of support, the traditional hub has needed to evolve. Too many parts of the system are mired by outdated workflows, manual processes, and siloed systems that lead to costly delays, patient drop-off, and widespread frustration.

Oversight of customer experience is limited to retrospective performance reviews rather than real-time insights into what patients need. By the time those numbers reach pharma teams, the window to course correct has already closed. And when patients abandon a prescribed therapy, everyone feels the pain: Patients forgo essential medications, health outcomes worsen, prescribers are frustrated, and pharma companies lose consumer trust, revenue, and momentum in their mission to improve care.

The good news? Things are changing. The traditional hub model is beginning to shift as technology matures and pharma companies look to play a more direct role in helping patients get what they need.

Today’s AI and automation tools now enable HIPAA-compliant redaction of personally identifiable data at scale, allowing organizations to extract meaningful insights without compromising patient privacy. These technologies have opened the door to real-time, compliant visibility into what’s happening across patient access programs — giving pharma the ability to step in, see what’s working, and take greater responsibility for improving the experience. 

In some cases, pharma companies are choosing to insource the technology stack — like the telephony and call recording infrastructure — even as other elements, like staffing, remain outsourced. 

One approach is to embrace a hybrid hub model that keeps the data and technology infrastructure in-house while outsourcing front-line staffing. This approach can allow for more reliable insights into customer sentiment, compliance challenges, and service quality on a consistent and ongoing basis.

Additionally, when a market event occurs, like an FDA safety alert, this approach can enable companies to immediately identify patient or provider concerns and adapt. It also makes it possible for  AI to handle transactional tasks like verifying insurance benefits, while keeping high-touch, sensitive interactions staffed by trained professionals. 

More manufacturers are taking a similar approach. With the right technology in place, pharma now has both the tools and the opportunity to take meaningful ownership of the patient experience and integrate greater control and visibility across their hub operations.

3 strategies for a more patient-centric journey 

We’ve entered a new era of expansive digital innovation and possibilities. But technology tools alone won’t be enough to live up to the promise of patient-centered care. 

Pharma companies need to think critically about what needs human oversight and what doesn’t — and develop a thoughtful strategy that balances support staff, processes, and technology. 

To that end, the industry should adopt the following three rules to refine the pharma support model:

1. Demand data transparency. Outsourced hubs tend to operate as black boxes that offer quarterly summaries and surface-level QA metrics while keeping other insights locked away. That approach was necessary in large part due to strict regulatory guardrails designed to protect patient-identifiable information. But those limitations are no longer in place. Compliance data analytics tools now make it possible for pharma companies to uphold regulatory standards while gaining real-time visibility into patient conversations, call center interactions, and experience trends.Unlocking that level of insight will require greater alignment, negotiation, and shared accountability with vendors and stakeholders across the healthcare ecosystem. But no matter how service operations are structured, it should be standard practice to demand clear ownership agreements, define expectations for data sharing, and build the infrastructure to monitor and act on insights in real time from every part of the process.

2. Strengthen governance before deployment. AI adoption is accelerating, but in many organizations, governance lags behind implementation. Without clear standards for compliance, bias mitigation, and safety, even the most promising tools can become liabilities, especially in a highly regulated and privacy-sensitive industry like healthcare. Before implementing any AI solution, pharma companies must ask critical questions about how the solution’s models are trained, how their performance is monitored, and what level of human oversight is in place. The questions they should be asking include: Who trained this model? What is the human-in-the-loop process? How are we monitoring for bias, drift, or compliance issues? Many life sciences organizations are now establishing formal AI governance committees to assess procurement decisions, evaluate model training data, and define performance standards. This needs to become a universal practice. 

3. Balance automation with empathy. Some touchpoints in the hub journey are transactional, while others are deeply human. The ability to distinguish between them is essential. Checking a benefits verification form or setting up a copay assistance program? AI can help process those forms quickly. Guiding a patient through a serious diagnosis or anxieties about a drug’s side effects? That is a job for human compassion, critical thinking, and emotional intelligence. The most effective strategies blend automation with human support, reserving tech for repeatable and transactional tasks, and keeping people in the loop where stakes and sensitivity are highest.  We should be asking questions like: Is this the right solution for this patient group? What touchpoints require a human touch? Where might we be unintentionally adding friction instead of removing it? 

With access barriers cleared, what comes next? 

Manufacturers now have a compliant path to greater visibility and direct accountability for the access experience. 

AI can now flag where patients are getting stuck — from enrollment delays to dropped calls — while also enabling new, compliant ways to analyze these interactions at scale. But surfacing the issue is just the first step. 

Turning insight into action requires people to retrain partners, reengineer workflows, and redefine success based on what actually matters to patients, not just what’s easiest to measure or monetize.

If we’re serious about improving patient access — and long-term health outcomes — pharma must look beyond quick fixes designed to prop up the next earnings report. Progress demands long-term investments and a strategy for companies to own the data, govern their tech, and rebuild support systems around real-world patient needs. We have the tools in hand. Now it’s time to do the work.

Photo: claudenakagawa, Getty Images

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