Closing the analytics gap: A new path to PSP maturity

For over two decades, Patient Support Programs (PSPs) have aimed to improve therapy access and support patients throughout their treatment journey. These programs remain central to how life sciences organizations engage patients beyond the prescription. Yet many PSPs still struggle to deliver consistent outcomes at scale.

Across programs at various stages of maturity, a familiar set of challenges tends to surface, such as regulatory hurdles, disconnected systems, manual steps, and limited visibility. Even well-established programs encounter these issues, making expanding reach or demonstrating value difficult. In fact, 95% of providers prescribing specialty medications have reported difficulty initiating therapy due to outdated processes and poor coordination.

Patients face these gaps too. In one survey, 82% reported delays in receiving therapy, and over half cited unclear insurance and cost details as the cause. It’s breakdowns of this nature that turn well-intentioned programs into frustrating experiences.

What many life sciences organizations seek now is a more responsive, structured model that adapts to real-time patient and provider behavior. Building maturity into PSP operations means organizing data, workflows, and teams in ways that reduce friction. 

Analytics plays a key role in making that possible. When embedded into everyday workflows, it equips teams with timely insights to reduce delays, improve coordination, and tailor interventions to actual patient needs. That’s what moves programs from sound design to consistent, measurable impact.

Why PSPs don’t stick

Why don’t PSPs stick? It’s not because they lack a sound foundation. Most are built on strong clinical reasoning and are well-aligned with brand objectives. But making them work in practice is another story.

On the ground, teams spend an enormous amount of time on tasks like enrollment, benefit verification, and documentation follow-ups. These processes are often manual, repetitive, and error-prone. Small errors lead to delays and rework. Over time, this makes it difficult to keep programs moving at the pace patients expect.

It’s no surprise that teams find it hard to scale when they’re already stretched thin. There’s little time left to focus on what actually advances progress, which is consistent, meaningful patient engagement. And coordination with providers and payers, while essential, is complex to sustain when bandwidth is limited.

From the patient’s side, many still don’t know these PSP programs exist. And even when they do, they’re not sure how to access support or who to contact between visits to the clinic. As a result, they’re left managing treatment, side effects, and coverage questions on their own.

Life sciences organizations need a way to make PSP workflows simpler, easier for teams to manage, and clearer for patients to navigate. That starts with better visibility: understanding how patients move through the program, where they drop off, and what they need next. With the right insights in the right hands, analytics helps deliver timely, personalized support while making better use of team resources. 

How analytics help and where it matters most

As matrix support programs bring in more teams, what keeps everything working smoothly is shared visibility. That’s where timely, practical use of analytics becomes essential.  

We often see that the roadblocks patients face shift depending on where they are in their journey. The kind of insights teams need shifts with them. When embedded into the right parts of the process, analytics can help teams respond faster, reduce errors, and shape better patient experiences. Here’s how that looks across each phase:

• Pre-enrollment: One of the common gaps in this stage is awareness of the program itself. Surveys show that 59% of patients have little to no awareness of PSPs, and only 10% find what they need online, despite 44% actively looking. Here lies the opportunity for organizations to find the right channels and content to reach the patients and lead them to the right programs.

Analytics can help PSP teams identify which outreach channels are working, understand where patients are dropping off, and refine the messaging to match patient needs. By segmenting audiences and tailoring communications, organizations can reach patients earlier and more effectively, so support doesn’t start too late.

• Enrollment and onboarding: Once a patient decides to enroll, things need to move quickly. But, manual methods of information gathering make it difficult. Data entry mistakes, unclear information, fragmented benefit verification processes, and coordination with other care departments delay onboarding. 

Here, analytics adds real value. Automated document checks and real-time validation can reduce errors upfront. Smart workflows can prioritize high-risk or urgent cases, and tracking productivity at this stage helps identify bottlenecks. This reduces delays and enables a smoother start to therapy.

• Access to therapy: Financial questions are often the biggest challenge at this stage. Patients may not understand what their out-of-pocket costs will be or how much their insurance will cover. For instance, 37% of patients are unaware of manufacturer copay cards that could ease their financial burden, or how copay cards work.

Analytics can support this phase by surfacing cases likely to face approval delays, streamlining benefit verification, and helping teams act early on denials. Real-time integration with payer systems and decision support tools can reduce friction, making it easier for patients to start treatment without unnecessary stress.

• Adherence and engagement: This stage tends to be the longest, often where patients need the most sustained support. While staying in therapy is ultimately the patient’s responsibility, expecting them to manage it alone doesn’t work. Ongoing guidance, check-ins, and resources play a major role in helping them complete their treatment journey.

What many life sciences organizations overlook is that this phase is also a rich opportunity to gather real-world insights. Data on adherence patterns and outcomes collected here can inform future interventions and expand access to therapy for other patients down the line.

This is where insights pay off the most. Predictive models help identify which patients might drop off and enable teams to act early, whether through personalized reminders, refill alerts, or timely follow-ups. One of the most meaningful uses of analytics we’ve seen is the automated flagging of adverse events. It gives teams the ability to respond faster and gain a clearer view of how treatments are performing in real time.

Is maturity within reach?

In conversations with PSP teams across life sciences organizations, a common question arises: how do we know if we’re using our data well, or just reporting on it? That’s often the starting point for maturity discussions.

To help life sciences organizations assess where they stand, we use a four-level framework:

Level 1: Are you compliant? 

You’re meeting reporting requirements, but data use is minimal. PSPs are still seen as a cost center.

Level 2: Are you business-centric? 

You’ve started using analytics to fix operational issues, reducing delays, improving turnaround, and smoothing workflows.

Level 3: Are you patient-centric?

You’re tailoring services based on patient behavior, segmenting effectively, and using data to drive more personalized engagement.

Level 4: Are you best-in-class?

Analytics is fully integrated across teams. Everyone has what they need to make better decisions, and it shows in your results.

Getting the foundation right

As analytics capabilities mature, we’ve seen PSPs become easier to manage, faster to adapt, and more effective in supporting patients at the right time. But getting there starts with clean, connected data, practical tools, and a clear view of how insights flow across the organization. When analytics is accessible to those working on the ground and behind the scenes, programs scale without adding complexity. This analytics maturity doesn’t happen overnight, but with the right data foundation and aligned teams, it’s absolutely within reach. This helps PSPs stick, grow, and deliver better care, better outcomes, and long-term value.