By Bob Seminerio
•
April 2, 2025
The landscape of pharmaceutical commercial strategy is undergoing a profound transformation, driven by the increasing availability and utilization of real-world data (RWD). Beyond traditional sales forecasts and market research, commercial teams now have access to vast amounts of information from everyday clinical practice, creating new opportunities to understand treatment uptake, patient journeys, and market dynamics in diverse patient populations. 1 Beyond Traditional Market Research For decades, commercial pharmaceutical teams have relied on primary market research, sales data, and prescription databases to drive business decisions. However, these traditional approaches have inherent limitations—they often provide only snapshots of market dynamics, lack patient-level insights, and may not represent the diversity of real-world treatment patterns. Real-world evidence (RWE) complements traditional commercial analytics by providing insights into how treatments perform in everyday practice across broader patient populations. This approach helps bridge the gap between carefully controlled research environments and the complexities of real-world market dynamics and patient journeys. The Growing Ecosystem of Real-World Data for Commercial Analytics The real-world data ecosystem encompasses multiple sources that collectively provide a more comprehensive view of market dynamics, patient journeys, and commercial opportunities: Electronic Health Records (EHRs) Electronic health records capture detailed clinical information, including diagnoses, treatments, laboratory results, and clinical notes. Their longitudinal nature allows commercial teams to track patient journeys over extended periods, providing visibility into treatment patterns, switching behaviors, and brand persistence that might not be captured in traditional market research. 2 However, EHR data presents unique challenges for commercial analytics. As one primary challenge, these records are primarily designed for clinical care and billing purposes rather than market analysis. Consequently, important commercial variables may be missing or inconsistently documented across different healthcare settings. Administrative and Claims Data Insurance claims data offers broad population coverage and structured information about healthcare utilization, medications, procedures, and costs. Unlike EHRs, claims data typically follows patients across different healthcare providers, offering a more complete picture of the patient journey and brand utilization. The standardized nature of claims data makes it valuable for comparing treatment patterns and market share across large populations. However, these datasets lack clinical detail and may not capture important nuances that drive prescribing decisions, such as disease severity or the reasoning behind treatment selection. Patient-Generated Health Data The rapid evolution of digital health technologies has enabled the collection of patient-generated data through smartphones, wearable devices, and patient-reported outcome measures. These tools capture real-time information about symptoms, functional status, and quality of life directly from patients in their everyday environments. This data source provides unique insights into patient experiences between clinical visits, potentially revealing unmet needs and treatment barriers that might not be apparent in traditional commercial research. The integration of this data with commercial information systems, however, remains a significant challenge. Transforming Commercial Decision-Making The integration of these diverse data sources is reshaping how pharmaceutical commercial decisions are made across several domains: Market Understanding and Opportunity Assessment Real-world data provides unprecedented visibility into how conditions are diagnosed and treated in practice, revealing gaps between clinical guidelines and actual practice patterns. This deeper market understanding helps commercial teams identify untapped opportunities and develop more targeted strategies to address unmet needs. By analyzing diagnostic patterns, referral flows, and treatment sequences across large populations, commercial teams can better understand the patient journey and identify key intervention points for brand messaging and support programs. Customer Targeting and Segmentation Real-world data allows commercial teams to develop more sophisticated approaches to customer targeting and segmentation based on actual prescribing behaviors rather than self-reported intentions. These insights help optimize field force deployment and marketing resource allocation to maximize return on investment. Advanced analytics methods applied to real-world data can identify physicians with high numbers of eligible patients who are not yet prescribing a particular treatment, revealing high-priority targets for outreach and education. Brand Positioning and Messaging Optimization By analyzing patterns of treatment selection across different patient types and prescriber specialties, real-world data helps identify the patient segments where a brand is performing well or underperforming. These insights support more evidence-based approaches to brand positioning and message refinement. 3 Challenges in Generating Reliable Commercial Insights Despite its potential, the use of real-world data for commercial decision-making faces several significant challenges: Data Quality and Standardization Perhaps the most fundamental challenge is the variable quality and lack of standardization across data sources. Healthcare systems use different coding practices, measurement approaches, and documentation standards, making it difficult to meaningfully combine and compare data across institutions. The absence of consistent data models and terminology creates barriers to data integration and limits the generalizability of commercial insights. Initiatives to create common data models and standardized analytical frameworks are essential but remain works in progress. Methodological Complexity Analyzing real-world data for commercial purposes requires sophisticated methodological approaches to address inherent biases and confounding factors. Without careful methodology, differences in prescribing patterns may reflect underlying differences in patient populations or physician preferences rather than true market dynamics. Advanced statistical techniques and careful study design are necessary to mitigate these challenges, but even the most rigorous approaches cannot entirely eliminate the potential for misleading conclusions from observational data. Commercial Application Considerations Commercial frameworks for evaluating and incorporating real-world evidence into business decision-making continue to evolve. Although leading pharmaceutical companies have established dedicated RWE teams, effectively integrating these insights into commercial planning processes remains challenging for many organizations. Looking Ahead Despite these challenges, real-world evidence will increasingly influence commercial pharmaceutical strategy and business decision-making. As methodologies mature and data quality improves, the integration of insights from traditional market research and real-world practice will provide a more complete understanding of market dynamics and patient needs. For pharmaceutical commercial teams, developing skills to critically evaluate real-world evidence and integrate these insights with other forms of market intelligence will be essential for maintaining competitive advantage in the years ahead. For more information on leveraging real-world evidence in healthcare decision-making or to discuss your organization's data strategy needs, contact The Pharma:Health Practice today. Footnotes " FDA Issues Draft Guidances on Real-World Evidence, Prepares to Publish More in Future ," U.S. Food and Drug Administration, January 2022. " Methodological challenges using routine clinical care data for real-world evidence: a rapid review utilizing a systematic literature search and focus group discussion ," BMC Medical Research Methodology, January 2025. " Real-world data quality: What are the opportunities and challenges ?" McKinsey, January 2023.