Key takeaways: Five Ways to Strengthen Data’s Impact
As organizations seek to evolve their patient support programs, five key actions can help bridge the data gap between analytics teams and patient care teams:
- Connect advanced analytics with frontline delivery insights. Break down silos so that learnings from data scientists inform daily operations, and observations from the field feed back into the data model.
- Respect and integrate qualitative assessment data. Incorporate psychosocial and behavioral observations into predictive models to capture a more complete picture of the patient experience.
- Empower frontline staff with accessible, real-time data tools. Give nurses, educators, and call center specialists intuitive dashboards and AI assistants that translate data into next-best actions.
- Measure longitudinal patient outcomes, not just transactional metrics. Track progress over time to understand how interventions influence adherence, satisfaction, and quality of life.
- Use data to anticipate predictable life transitions. Identify moments, such as aging out of parental insurance or retirement, when proactive engagement by PSP can prevent therapy disruption.
Data will always be essential in measuring commercial performance. But for the situations that fall outside the typical pathways, data’s value lies in its ability to illuminate the human story behind the numbers—why patients disengage, what motivates them to persist, and how programs can better respond. To learn more, keep reading. You can also connect with one of our Patient Support Services operational leads to explore how IQVIA can help you better define and optimize your data strategy.
Introduction
For some patients, the pathway to getting on therapy is relatively straightforward—benefits are verified, prior authorizations approved, and treatment begins. Yet for others, the process can be anything but simple. Patients often face obstacles such as financial barriers, administrative hurdles, lack of understanding, or personal challenges that delay and disrupt the process and ultimately prevent them from starting and staying on treatment.
When the “system” fails to work for these patients, it becomes the mission of patient support programs (PSPs) to understand why and, when appropriate, find ways to help them start and stay on the therapies they need. Meeting that mission is inherently complex, with circumstances as varied as the people served.
Data can be invaluable in informing this work, but only when it’s centered on patients. Too often, analytics teams focus on standard performance metrics, missing the deeper human context that influences behavior and drives adherence. The most significant insights are gained not from analyzing averages, but from identifying and examining outliers —the patients whose journeys deviate from the expected path—and offering solutions to help them get back on track.
A Data Disconnect
Despite advances in analytics, a disconnect often exists between the insights generated by advanced analytics teams and the realities faced by frontline patient support teams. Data outputs and dashboards are typically designed for leadership reviews, not for the day-to-day decision-making that happens in call centers, nursing hubs, and field support teams.
The result? Insights that don’t translate into action. Frontline teams—the people engaging with patients, caregivers, and healthcare providers—are often left to rely on instinct and experience rather than evidence. When insights fail to reach the people who need them most, programs risk stagnating in reactive mode rather than proactively shaping patient outcomes.
Given today’s advances in artificial intelligence and machine learning, that’s a missed opportunity. These tools can illuminate patterns that point to emerging risks, if the right people have access to the right data, at the right time.
What Today’s Data Misses
Most PSPs still rely heavily on transactional metrics: average speed of answer, call handle times, benefits verification turnaround times, customer satisfaction (CSAT) and engagement rates. While these measures are important, they only tell part of the story. For instance, a long support call that resolves a patient’s issue is far more valuable than a short call that ends in confusion or frustration. IQVIA’s own data show that shorter call lengths don’t necessarily correlate with higher satisfaction. In many cases, extended interactions that achieve resolution result in higher patient satisfaction.
These metrics also tend to overlook psychosocial factors such as patient motivation, health literacy, and confidence, that equally influence treatment outcomes. These human variables may be harder to quantify but are far from impossible to detect.
Analysis of unstructured data, such as call transcription, can reveal critical insights into patient motivation and confidence. For example, patterns in call frequency and tone may signal whether a patient is proactively engaged or facing challenges. Frequent touches or recurring issues may indicate a more complex patient journey, signaling a call-to-action for higher-touch or proactive support by PSP.
When organizations focus solely on transactional indicators, they risk missing the very signals that show whether a patient is truly prepared and motivated to independently navigate their treatment journey.
Data as a Patient Empowerment Tool
To truly bridge the data divide, organizations must reframe the purpose of analytics not merely as a performance measurement tool, but as a patient empowerment engine. When designed around patient needs, data can help ensure the “right resource, at the right time, in the right way.”
In practical terms, this might mean:
- Natural language queries empower PSP teams to ask real-time, context-aware questions and proactively adjust support strategies to match the complexity of each patient’s journey.
- Support stratification models that use analytics to determine the optimal support pathway, scaling interventions based on unique needs of each patient, such as routing high-complexity cases to nurse educators and low-complexity cases to digital channels.
- Qualitative data capture, where frontline staff record patient assessments and goals—like a grandmother’s desire to regain energy to play with her grandchildren—to guide future interactions and reinforce behaviors that will motivate adherence.
These approaches move data beyond dashboards and into daily practice, creating an ecosystem where insights actively guide patient engagement and resource allocation.
Conclusion
Bridging the data divide in patient support programs requires more than technology. It demands empathy, integration, and alignment across every layer of the organization. By empowering the people on the front lines with actionable insights, patient support teams can evolve from reactive problem-solvers into proactive partners in care.
Ultimately, data that begins and ends with patients doesn’t just enhance adherence or satisfaction. It builds trust, improves outcomes, and redefines what meaningful support truly looks like.
Interested in strengthening your patient support strategy?
Connect with IQVIA’s Patient Support Services team to learn how our experts can help you transform data into actionable insights that improve access, adherence, and patient experience.