How AI and Real-World Data Are Transforming Pharma Commercialization
Author:
Verana Health
Following years of very costly effort, regulatory approval may feel like a triumph. And while the milestone is worth celebrating, the ongoing journey from lab to patient is really just entering a new phase: commercialization.
Unfortunately, the common approach to commercialization often includes outdated systems, fragmented data, and static planning cycles. The consequence: a persistent disconnect between market opportunity and commercial execution, with many organizations struggling to identify the right prescribers, engage patients effectively, or respond quickly to market shifts.
The good news is that real-world data (RWD) is increasingly shaping a holistic view of the patient journey, and helping pharmaceutical companies gain insight to fine-tune strategies and improve patient access.
Paired with artificial intelligence (AI), RWD has already demonstrated potential to make the drug development process more efficient, bring drugs to market faster, and improve cost efficiencies.
Now, RWD is helping pharmaceutical companies better understand disease progression, patients’ responses to drugs, long-term treatment patterns, and other critical aspects of commercialization.
The Value of RWD
RWD is a vast resource encompassing an array of disparate datasets, including information from medical claims, electronic health records (EHRs), and wearable devices. Hidden within it are details revealing the reality of the patient healthcare experience—if you can decipher it.
Much of RWD is unstructured, meaning it is not in a consistent format or easily queryable. A prime example is the clinician notes of EHRs. The difficulty in efficiently deriving validated meaning from this unstructured resource has limited its utility in research and clinical decision-making.
The advent of AI, however, with advanced machine learning (ML) and natural language processing (NLP) models, makes it possible to curate and harmonize unstructured RWD at scale and with unprecedented speed.
For pharmaceutical companies engaged in commercialization, this opens up continuous monitoring of patient treatment patterns, prescriber behaviors, and their evolution.
Are patients filling their prescriptions? Do they adhere to treatment? What triggers them to abandon treatment? Are many switching to another brand?
This insight enables companies to assess the competitive landscape, develop an understanding of how a therapy is performing relative to competitor treatments, and track new product launches, market share shifts, and therapy preferences among prescribers.
Armed with this information, pharmaceutical companies have a strategic basis to make informed decisions and identify opportunities to differentiate their products based on real-world usage.
Treatment Patterns, Brand Switching and Prescriber Mapping
By monitoring market dynamics, tracking brand initiations, switches, and discontinuations, and analyzing treatment frequency, pharmaceutical companies gain valuable insights that empower product positioning more effectively than inherently flawed market surveys.
Understanding why patients and prescribers switch brands is crucial for refining strategies, improving product positioning, and increasing market share. Yet it remains one of the most common — and least answered — questions in pharma.
Brand switching can be driven by effectiveness, cost, side effects, treatment burden, or prescriber preference. Prescribers’ motivation for switching patients from one treatment to another can’t always be inferred from structured data, and instead resides in unstructured clinical notes. Now large language models (LLMs) make it possible to access timely insight to the drivers of treatment switching.
As more generics and biosimilars enter the marketplace, the lower out-of-pocket costs may make patients more likely to adhere to treatment. RWD can provide insights into how many patients are switching from their originally prescribed treatment to a generic or biosimilar and the cost differentials. By surfacing these insights at scale, RWD supports more informed product strategy, market access, and medical engagement decisions.
RWD is also becoming useful for understanding prescriber behavior and optimizing launch messaging and prescriber targeting.
By understanding trends and practice patterns, pharmaceutical companies can tailor communication strategies to reach prescribers who are influential in their community networks, group practices, or larger academic medical centers.
RWD can identify prescribers who exhibit strong loyalty to particular therapies, enabling companies to engage with them to strengthen brand affinity—and identify competitive threats by tracking prescribers volume of procedures or prescriptions across brands, and analyzing changes in prescribing behavior.
Bridging the Commercialization Gap
Across the commercialization specter, RWD is providing a comprehensive view of market dynamics and patient experiences, leading to actionable insights and competitive analysis.
With research citing use of RWE in commercialization as a top ROI amongst pharma companies, the hype of RWD and AI in commercialization is giving way to execution. Organizations that act decisively can gain a meaningful edge, ensuring that their strategies remain relevant and proactive with actionable insights.
Failing to leverage RWD puts any commercialization strategy on shaky ground. Harnessing the power of RWD and getting that data to talk gives pharmaceutical companies a holistic view of the patient journey, which can help boost market share, maximize ROI and, most importantly, increase patient access to life-saving therapies.
Read the full White Paper.
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