The Value of Analyzing Physician Drug Adoption Rates
Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of Using Claims Intelligence in Healthcare. The presenter is Abhishek Gupta, Chief Data Scientist at InetSoft.
Beyond the value of analyzing physician drug adoption rates, you can think about the kind of the affiliation structures that we've talked a little bit about that really helped to inform what your drug launch strategies should be based on, and how loosely or tightly aligned a doctor may be within our particular system. You also have the contact information for physicians whether that'd be the emails that we have on hand, the addresses where they're practicing for their primary practice location, or even direct dials. That really helps to inform and allow you to have that efficient and targeted outreach strategy.
Then finally you are able to incorporate all of their prescribing data into your workflows. Being able to think about that prescribing data within the context of the therapy areas or the indications that are important to you allows you to have a new dimension to think about that affects the success of a launch before you actually go to market.
Throughout this presentation, I've been able to show you a couple of different examples of pharmaceutical launch analytics and how you can actually get great access to commercial claims data and use that to support your strategic business initiatives. The in product integration makes this data easy to use, but sometimes it's important to really accelerate what your analytics journey.
Analytic Capabilities from Healthcare Claims
This expands what some of those analytic capabilities are. You can go ahead and define and develop what are some complicated analyses that might bring you additional insight and intelligence within the commercial healthcare claims landscape. With that we're going to head into our Q&A portion of the webinar.
Great, thanks. First question here: Can you tell me more about the sources of claims data used in the analyses?
This claim data comes from multiple different clearing houses that actually operate in the market today. We combine that data with other sources, including claims data from the Medicare program to come up with a robust all payer claims data solution. When we take a look at that data asset, we understand that there's no kind of significant bias in that data, meaning there is great representation across all geographies and across different periods of time. Beyond that, data goes back to January 1st through the present time, which enables us to really deliver those real time views, but also to analyze and trend that data over the past three calendar years.
Great, another question here. You shared insight on being able to drill into referral patterns. Can you tell us more about that part of the offering?
Absolutely, we use our claims data to run a really data-driven referral analysis that helps our customers understand how patients are moving between healthcare organizations and healthcare professionals. This analysis not only helps to demonstrate the real relationships that are in place based on claims experience, but what it also does is really help to demonstrate the strength of those relationships by quantifying and counting referrals over a couple of various windows of time.
That really starts with a 60 day window and can escalate up to 120 days from that analysis. Through the data, we can really kind of slice and dice these referrals by a number of different factors, which would be kind of by time, by direction, by type of provider, to really help inform what your targeting strategy might be.
Example Based On Pharmaceutical Benefits Claims Analytics
When organizations tap into pharmaceutical benefits claims data, they gain access to one of the richest and most behaviorally revealing datasets in the healthcare ecosystem. Unlike survey responses or self‑reported prescribing intentions, claims data captures what physicians actually do—what they prescribe, how often, for which patient populations, and in response to which clinical circumstances. This makes it an invaluable foundation for understanding real‑world drug adoption patterns. A well‑constructed analytics workflow can transform millions of raw claims into a clear narrative about how a therapy is performing, where adoption is accelerating, and where barriers still exist.
A typical analysis begins by examining longitudinal prescribing behavior at the physician level. Claims data allows analysts to track the moment a prescriber first adopts a therapy, how quickly their prescribing volume grows, and whether that growth stabilizes, accelerates, or declines over time. These patterns often reveal distinct behavioral archetypes, early adopters who embrace new therapies within weeks of launch, cautious evaluators who wait for peer validation, and resistant prescribers who require targeted education before shifting away from entrenched habits. By segmenting physicians into these behavioral groups, commercial teams can tailor outreach strategies that match each segment’s motivations and concerns.
Another powerful dimension of pharmaceutical benefits claims analytics is the ability to map adoption trends across therapeutic subpopulations. For example, a drug may gain rapid traction among endocrinologists treating complex metabolic disorders but lag among primary care physicians managing mild cases. Claims data makes these differences visible by linking prescriptions to diagnosis codes, comorbidities, and treatment histories. This enables analysts to identify which patient cohorts are driving early adoption and which remain underpenetrated. Commercial teams can then refine messaging to highlight clinical advantages that resonate with each specialty or patient profile.
Geographic variation adds yet another layer of insight. Claims data can be aggregated at the ZIP code, county, or metropolitan level to reveal regional pockets of strong adoption and areas where prescribing remains stagnant. These patterns often correlate with local formulary restrictions, competitive dynamics, or the influence of regional health systems. For instance, a therapy may show strong uptake in regions where a major integrated delivery network has added it to its preferred drug list, while neighboring regions with more restrictive payer policies lag behind. By overlaying payer mix, formulary tiering, and reimbursement trends, analysts can pinpoint the structural factors shaping adoption and identify where market access interventions may be needed.
Claims analytics also supports deep investigation into switching behavior—one of the most telling indicators of a therapy’s competitive strength. By tracking which drugs patients were using before initiating the new therapy, analysts can quantify displacement patterns and understand which competitors are losing share. They can also measure persistence and adherence, identifying whether patients remain on therapy for the expected duration or discontinue prematurely. These insights help commercial teams understand not only how many physicians are prescribing the drug, but how well the therapy is performing in real‑world clinical practice.
Another compelling use case involves analyzing referral pathways that lead to therapy initiation. Claims data can reveal which specialists are diagnosing conditions, which providers are initiating treatment, and how patients move through the care continuum. For example, analysts may discover that a significant portion of new starts originate from referrals between primary care physicians and a small cluster of high‑volume specialists. Understanding these referral dynamics allows commercial teams to focus educational efforts on the providers who exert the greatest influence on treatment decisions, rather than spreading resources evenly across the entire provider universe.
Pharmaceutical benefits claims analytics also plays a critical role in forecasting future adoption. By modeling historical uptake curves, competitive launches, seasonal patterns, and payer policy changes, analysts can project how prescribing behavior is likely to evolve. These forecasts help commercial leaders allocate field resources, plan inventory, and anticipate revenue trajectories. They also support scenario planning—evaluating how adoption might shift if a competitor receives new indications, if a payer tightens prior authorization requirements, or if new clinical evidence emerges.
Finally, claims‑based analytics empowers organizations to evaluate the effectiveness of their commercial strategies. By comparing prescribing trends before and after targeted interventions—such as educational campaigns, field force redeployment, or payer negotiations—teams can quantify the impact of each initiative. This creates a continuous improvement loop where insights from claims data inform strategy, strategy drives action, and subsequent claims data validates or refines the approach. Over time, this disciplined, data‑driven cycle becomes a competitive advantage, enabling organizations to respond faster, target more precisely, and support physicians with the information they need to make confident prescribing decisions.