Life sciences commercial teams face an increasingly complex challenge in understanding the true market treatment distribution in specialty care markets, such as how to engage the right healthcare providers (HCPs) with the right treatment at the right time. As specialty therapies become more sophisticated, especially among personalized oncology treatments and rare disease interventions, more innovative methodology to enhance traditional HCP targeting methods is no longer sufficient on its own.

One of the core issues in specialty therapeutic areas lies in fragmented data landscapes. These create significant blind spots for analysts looking to draw accurate insights. Pharmaceutical companies have a greater imperative to identify the right HCPs and help them with unprecedented precision and speed to maximize patient reach and therapeutic impact.

Understanding the challenge

When targeting specialty care physicians, we find that post-pandemic engagement windows have shrunk dramatically, while precision requirements for targeting have only increased. When every conversation with a specialist could represent the difference between reaching eligible patients or missing critical treatment opportunities, commercial teams must utilize a wide range of metrics instead of relying on volume-based metrics alone.

This data fragmentation manifests differently across therapeutic areas but creates similar challenges. In rare diseases, patient populations may be scattered across multiple specialties, making it difficult to identify diagnosing and treating physicians through traditional claims data alone. In other areas, patients often move between primary care and specialist settings, creating gaps in treatment visibility.

The emergence of digital behavior intelligence

Digital behavior patterns are emerging as a promising candidate for generating leading indicators associated with future prescribing behavior. Unlike historical diagnosis and treatment data that show what already happened, physician digital behavior provides real-time insights. This enables engagement strategies that are proactive rather than reactive.

Data regarding the digital research behavior of HCPs captures three critical categories of engagement.¹ First are their disease awareness activities related to therapeutic areas of interest. Next are their visits to manufacturer websites, which track when physicians visit branded product websites, indicating interest in specific treatments. And, finally, there are competitor research activities, where HCPs are looking for alternative treatment options.

Analysis shows that physicians’ digital research patterns have predictable value for treatment initiation across various specialty areas.^1-3 In oncology, for instance, this research might include toxicity management, hematology conferences, or specific complications. In rare diseases, physicians often research disease manifestations, diagnostic criteria, or treatment protocols before making treatment decisions for their patients. For specialty metabolic conditions, research patterns might focus on monitoring requirements, drug interactions, or patient management protocols. These digital footprints signal potential future prescribing behavior across therapeutic areas.

Building next-generation predictive models

Advanced machine learning approaches can integrate multiple data sources to create more sophisticated and accurate targeting models. By combining traditional claims data with digital behavioral insights, pharmaceutical companies can develop predictive algorithms that significantly outperform single-source approaches.²

In practice, this integration involves sophisticated engineering of digital research patterns, including analyzing the frequency of relevant research, their timing relative to treatment decisions, and other contexts, like physician specialty. Claims data integration provides the clinical foundation and could surface predictive features like chemotherapy treatment patterns, specific targeted therapy use, or diagnostic tests ordered.

Case studies and real-world applications

Recent implementations of integrated predictive models demonstrate substantial improvements in targeting precision. When comparing traditional approaches to enhanced models incorporating digital behavioral data, the results show marked improvements in identifying physicians likely to initiate specific treatments.

Taking the targeting of oncologists likely to start treatment with CAR-T as an example, validation studies show that targeting top-scored physicians from predictive models yields precision rates of 60% or higher, compared to baseline population rates of less than 2%.³

The improvement becomes even more pronounced when examining lift metrics, which compare the performance of the model with that of the anticipated baselines. The model incorporating digital behavioral data demonstrated 44 times better precision compared to a rules-based baseline approach, which illustrates the substantial advancement that sophisticated analytics brings to targeting strategies.³ This level of improvement means commercial teams can identify and prioritize HCPs with unprecedented accuracy and focus their efforts where they will have the greatest impact.

The technical implementation of these models involves sophisticated data processing. Data scientists apply systematic filtering to identify the most predictive signals. In the CAR-T model, this starts with keyword mapping of more than 14,000 diseases and topic-related terms captured from physician digital behaviors.³ This filtering process keeps only topic words with prevalence rates above 10% at the event level and groups highly correlated terms to avoid redundancy. The final model incorporates around 308 data-driven digital behavior features that demonstrate meaningful predictive power in anticipating the initiation of CAR T-cell treatment.³

From theory to practice: Implementation insights

Digital behavioral insights enable several practical improvements in commercial strategy. They support behavioral triggers for precision timing, allowing field teams to engage physicians when they are actively researching relevant topics rather than relying on predetermined call schedules. Optimizing timing can dramatically improve the relevance and effectiveness of HCP interactions.

While CAR T-cell therapy provides a concrete example of this approach, the methodology applies broadly across specialty care. In rare disease targeting, similar digital behavior patterns emerge around disease-specific conferences, diagnostic tools, and treatment protocols. The same approaches to feature engineering and analysis of research frequency, timing, and specialty context prove effective whether targeting physicians likely to treat genetic disorders, complex metabolic conditions, or advanced oncology therapies.

Sophisticated behavioral recognition makes personalization at scale possible. When field teams understand what specific topics physicians are researching, they can tailor their conversations accordingly across therapeutic areas. For instance, if digital research patterns indicate a physician has been investigating rare disease diagnostic criteria, representatives can prepare conversations about differential diagnosis support.

The future of commercial strategy is more sophisticated

The evolution of commercial strategy requires embracing predictive approaches that leverage multiple data sources and rethink traditional targeting paradigms. Success demands building new capabilities around integrated data insights, particularly for challenging therapeutic areas. As therapeutic complexity increases, organizations must cultivate open mindsets toward innovative data sources beyond traditional claims and electronic medical records data to reach the right HCPs at the right time and meet evolving business imperatives.

Göksu Dogan is Sr. Principal, Patient Analytics and AI; and Li Zho is Sr. Director, Data Science and Advanced Analytics; both with IQVIA

References

1. IQVIA. AIM XR: Unrivaled data and insights to power exceptional HCP experiences. IQVIA Fact Sheet. 2023 December 22, 2023. https://www.iqvia.com/locations/united-states/library/fact-sheets/aim-xr-unrivaled-data-and-insights-to-power-exceptional-hcp-experiences

2. IQVIA. Available IQVIA data. The IQVIA Institute. Cited September 3, 2025. https://www.iqvia.com/insights/the-iqvia-institute/available-iqvia-data

3. IQVIA. AI/ML DMD powered CAR-T predictive solution. IQVIA internal data. April 2023.