With Greater Data Comes Greater Responsibility – Data Insights in Copay and Patient Support Programs
The importance of real time, decision-driving data analytics in copay and patient support programs is growing. Machine learning, AI, and digital-first have all contributed to the rise of access to patient data. But more data used in the same old way does not mean more insights. The volumes of data that copay and patient support services have nowadays has brought more responsibility - not only to protect that data (acting on patient data starts with verifying consent across every partner), but to use it in a way that brings about change in patient access. Have you wondered if you are measuring the right metrics for your programs? Discover what copay and PSP experts have to say about which metrics are meaningful and how to get to them successfully.
Data Points in a Patient’s Journey: What Metrics to Look For
A connected patient data journey means more opportunities to truly understand the barriers in access and address gaps in the programs. However, having access to so many data points can be overwhelming and it’s easy for true insights to get lost in the noise.
Sometimes the data for the most obvious questions – what share of patients face a PA, what share get denied and must appeal – does not tell the full story of what patients are experiencing. These numbers have a cost that patients, physician offices and on-the-ground teams pay, even when it never shows up in a report: days from submission to determination, rounds of resubmission, and the staff hours and phone calls it takes to keep things moving.
“Two payers can have identical approval rates and represent completely different burdens. That burden is what actually predicts abandonment; patients rarely drop off because they were formally denied, they drop off because the process outlasted them.” - Hillary Gretton, Director, Data Strategy at Claritas Rx
For Patrick O’Neal, Vice President, Market Access at Millicent Pharma, one particularly valuable metric has been time-to-therapy, including the time patients spend at key stages between prescription generation and therapy initiation. Other leading indicators can include prior authorization turnaround times, benefit verification delays, prescription transfer activity and pharmacy fulfillment bottlenecks. Patrick emphasizes that the greatest opportunity often exists earlier in the patient journey, where access barriers first emerge.
“By integrating insights from specialty pharmacy operations, patient support services and copay program utilization, we identified a population of patients who had both coverage and financial assistance available yet were not initiating therapy. A deeper analysis revealed that delays were occurring across multiple touchpoints, including prior authorization processing, pharmacy handoffs, and uncertainty surrounding final patient out-of-pocket responsibility. This reinforced an important lesson: patients rarely disengage because of a single barrier. More often, friction accumulates across the access journey, creating delays that ultimately impact therapy initiation and outcomes.”
- Patrick O’Neal, Vice President, Market Access at Millicent Pharma
Hillary also recommends tracking time between milestones as an indicator of potential drop-off. In her experience, a patient who moves unusually slowly between two milestones is often showing an early signal of risk, which gives teams a chance to intervene while the patient is still in motion. But “unusually slow” has to be defined in context. Expected timing varies by therapeutic area, brand, and patient population, so an interval that looks long for one brand may be perfectly normal for another. Benchmarks need to be set for each program, not applied across the board. The intervention window also stays open only if the data is fresh enough to act on: specialty pharmacy and program data typically arrives within 1–2 days, while claims data lags well behind. Identifying where slow intervals concentrate, whether by payer, plan type, or specialty pharmacy, can help teams see when a systemic fix is needed, such as proactive documentation, targeted field education, or a change in how cases are handled. As Hillary notes, “without the attribution, every case looks like a one-off,” and teams end up solving the same problem over and over.
The other critical consideration is resource allocation: are sufficient funds budgeted for the program, and do projections indicate a need for adjustment? Looking at the average disbursement amounts in copay programs and the reasons patients cannot be supported reveals where the systems need to be modified, according to Peter Avalos, VP Market Access and Pricing at Summit Therapeutics.
“In a previous role of mine, we had a program that covered only access barriers. If a patient didn't have insurance, then the program would work, a very traditional approach. But as more data came in, we found out that there were other reasons that could drive a patient to be on a patient assistance program. The program was then updated to include situations when a patient has insurance, but they can't afford the secondary and they're on a government type of insurance. Originally, the copay program couldn't support that scenario. We updated our program, our business rules, to allow for such patients to be evaluated because of the data insights we received.” - Peter Avalos, VP Market Access and Pricing at Summit Therapeutics
Prepare for the Future: From Post-Fact to Decision-Informing Reporting
Transforming a siloed data system into an organized and consistent data architecture is no small feat, but it is one that can fundamentally elevate your understanding of your patients, their circumstances and the effectiveness of your processes and programs.
The first step to more predictive analytics is getting to know your vendor and setting up an open line of communication. Avalos suggests a proactive approach to vendor relationships where organizations and vendors are true partners in creating more accessible care.
“Keep in the know about what vendors are doing, their new capabilities and data, new ways of looking at business. Present your gaps and your problems, admit when you are missing data or feel blind and ask your vendor how they have overcome these barriers.” - Peter Avalos, VP Market Access and Pricing at Summit Therapeutics
Once you have found a trusted partner, spend some time understanding what question you want your data to answer. O’Neal recommends keeping your initial scope manageable. Rather than trying to predict every outcome in the patient journey, focus on one or two high-value questions. For example: Which patients are most at risk of not starting therapy? What factors are contributing to refill delays? Success in these focused use cases often creates momentum for broader predictive analytics initiatives.
“First, invest in data integration so that access, affordability, and fulfillment information can be viewed as part of a single patient journey. Second, establish strong governance and common definitions, so teams can trust the data and act on it confidently. Third, build analytics that identify leading indicators and trigger operational actions to address the issue, rather than simply measuring outcomes after the fact.” - Patrick O’Neal, Vice President, Market Access at Millicent Pharma
After you've set your expectations and identified the story you want to tell, evaluate your patient journey and the metrics you report on. Ask yourself: are you focusing on metrics that explain what happened rather than why it happened?
“Start by identifying the handful of moments in the patient journey where a decision actually gets made, whether to escalate a case, flag a payer for outreach, or trigger a hub intervention, and work backward from there. The practical shift is building status and timing data that updates daily rather than weekly or monthly, so a program can see a patient trending toward drop-off while there's still time to intervene, not just confirm afterward that they did. It's less about having more data and more about surfacing the right signal, on the right patient, at the moment someone can still do something with it.”
- Hillary Gretton, Director, Data Strategy at Claritas Rx
The role of data insights in copay and patient support programs has shifted from providing retrospective overviews to enabling real-time analytics that empower teams to intervene in the moment, preventing patient drop-off and improving program offerings. Whether you're looking to enhance your data architecture or seeking to convince stakeholders to invest more in data workflows, Patient Pathways 2026 offers an opportunity to hear directly from Hillary and Patrick on maximizing the value of data in the patient support industry.
Join Hillary Gretton, moderator for a panel on “Harnessing AI, ML and Next Best Action to Elevate Patient Support Services Strategy” (Thursday, 19 November at 8:10am).
Listen to Patrick O’Neal’s presentation on “Reimbursement Analytics – Unlocking Insights from Claims Data” (Tuesday, 17 November at 11:30am).
Patient Pathways is the leading patient access event in Philadelphia, with over 500+ attendees across four days. It combines the Patient Support Services Congress and Copay, Reimbursement and Access Congress into one comprehensive event and is the only event on the market covering copay, reimbursement and patient services.