As real-world data (RWD) becomes more central to healthcare decision making, evidence generation is playing an increasingly important role, from early identification to treatment optimization and beyond. But its impact goes far beyond supporting regulatory approval or market access.
We caught up with Chris Harvey, VP of RWD Product and Offering Management at Norstella, Panalgo’s parent company, to talk about how evidence generation is helping organizations better understand patients, uncover unmet needs, and inform decisions across the healthcare ecosystem.
Q: How is evidence generation helping identify patients and uncover new indications?
A: There are a couple of ways this plays out. As pharma organizations generate evidence using deeper clinical data sets, they’re able to identify which patients within a population have unmet needs, whether that shows up as lack of treatment options, poor ongoing response, treatment discontinuation, or low medication adherence.
Historically, teams relied on structured claims data and metrics like time to treatment discontinuation or medication possession ratio. The problem is those metrics don’t tell you why a patient stopped their medication, whether their needs simply weren’t being met or something else was going on.
What we see now is teams leveraging richer clinical data sources, including EMR and physician notes, to get at patient and physician sentiment. That context is what allows organizations to pinpoint where current therapeutics are falling short and where new indications or investments make sense.
Q: How is RWD making a difference in clinical trials, particularly in finding the right sites?
A: There are data sources in the industry that combine clinically enriched patient data with provider-level information, things like regional and national provider identifiers, and hospital and health system affiliations. The way teams use this is by applying inclusion/exclusion criteria to a patient population within a real-world data source, then linking those patients back to the providers treating them. That tells you which physicians and health systems are seeing the patients who would qualify for your trial.
You can also layer in investigator experience data. We work with our Citeline organization for that, to assess whether a site has a track record with similar studies.
What’s equally important is using RWD upstream during trial design. If you apply your inclusion/exclusion criteria to real-world data before you finalize the protocol, you can see how those criteria affect your ability to recruit, map out regional patient concentrations, and make trade-off decisions, adjusting criteria if needed, before you’ve committed to a design that makes enrollment unnecessarily difficult.
Q: How does RWD improve post-approval safety analysis?
A: Once a drug is approved, teams often need to set up Phase IV trials or patient registries to track safety over time. RWD adds a layer on top of that. You can apply industry-standard code mappings like ICD-10 categories to pre-specify events of interest and monitor their incidence across a broad population. Or you can run a more automated, hypothesis-generating approach, looking at the incidence of every billed diagnosis and flagging anything that deviates from what you’d expect compared to a standard of care.
If you have access to clinical data, you also get the reason behind discontinuation, whether a patient stopped because they completed a course of care, ran into insurance issues, or experienced an adverse event that wasn’t fully visible in the clinical trial.
The other area where RWD is genuinely valuable is responding to regulators. When a regulator flags a potential safety signal, often based on anecdotal or limited evidence, RWD lets you take that signal and test it against a real-world population at scale. I’ve worked on this directly with our Panalgo platform, where we helped companies rapidly produce incidence and prevalence studies in response to regulatory inquiries. When that evidence is already structured and available, you can respond quickly, and the studies have been accepted by regulators to determine whether adverse events warrant label changes.
Q: Overall, how is evidence generation enabling better insights and decision-making for life sciences companies across the full patient life cycle?
A: The use cases span every stage. Early on, RWD helps characterize unmet need, and that same evidence can be formalized to support an Investigational New Drug application, giving regulators the context they need to approve patient recruitment for a study. From there, you move into trial design, site identification, and recruitment planning, which we’ve covered.
Post-approval, beyond safety, the big one is payer access. A drug getting approved doesn’t mean a payer will reimburse it. Payers want to see the cost-benefit case: not just clinical efficacy, but how the drug impacts burden of illness and resource utilization over time. RWD has long been the backbone of health economics and outcomes research for exactly that purpose. More recently, we’re seeing clients link payer policy and formulary restriction data to RWD (for example, assessing how a prior authorization requirement affects not just whether patients receive the drug, but what happens to their outcomes as a result). We did this recently using our MMIT data linked to claims.
There’s also growing use of RWD as external control arms, particularly in oncology and rare diseases, to accelerate approval timelines where recruiting a traditional control group isn’t feasible.
And on the commercial side, a strong evidence strategy helps drug reps and field teams focus their conversations on the information that’s actually relevant to the patients a physician is treating, which ultimately benefits patients too.
Ready to find out how Panalgo IHD and NorstellaLinQ can fuel a better understanding of the patient journey? Contact us.