Guest Column | March 25, 2026

From Real-World Data To Real-World Impact: Building The Evidence Capability Pharma Actually Needs

By Hayden B. Bosworth, Ph.D., Steven C. Grambow, Ph.D., and Drew Narayan, MBA

Data, business analysis-GettyImages-1652005966

Real-world evidence (RWE) has rapidly evolved from a supplementary analytic approach to a central component of pharmaceutical development, regulatory decision-making, and market access strategy. Regulators increasingly accept fit-for-purpose real-world data to support safety monitoring, effectiveness assessment, and even regulatory submissions when methodological rigor is demonstrated.1,2 At the same time, health systems and payers increasingly expect evidence that therapies deliver meaningful outcomes outside tightly controlled trials.3,4

Despite unprecedented growth in healthcare data sources, including electronic health records, claims databases, registries, and digital health streams, many organizations still struggle to translate real-world data sets into actionable decisions. The constraint is rarely the volume of data. Instead, it lies in the organizational capability to frame decision-relevant questions, generate defensible analyses, and ensure findings influence clinical adoption and patient outcomes.

The consequences of underdeveloped RWE capability are significant. Weak evidence strategies increase regulatory burden and downstream study costs, undermine payer positioning, fragment launch execution, and compress revenue generation by limiting pricing power and slowing uptake. In an environment where comparative effectiveness, value-based contracting, and outcomes-based reimbursement are expanding, the ability to produce defensible, decision-aligned RWE is increasingly tied to revenue durability and competitive differentiation.

Why More Data Has Not Automatically Produced Better Decisions

Pharmaceutical organizations now operate in a data-rich environment, yet translating those data into decision-grade evidence remains complex. Observational analyses must address confounding, selection bias, and data completeness, while still producing findings interpretable by regulators, clinicians, and payers. Methodological rigor alone is insufficient if evidence cannot be operationalized or communicated effectively.

This challenge reflects a broader shift in evidence expectations. Randomized trials remain essential, but stakeholders increasingly recognize that real-world effectiveness, utilization patterns, and treatment sustainability determine whether clinical innovation achieves its intended public health impact.5–7

Organizations that succeed in this environment treat RWE not as a technical output but as a cross-functional capability integrating epidemiology, statistics, clinical expertise, regulatory strategy, and implementation planning.

Implementation Science As The Bridge Between Evidence And Outcomes

Even when strong clinical evidence exists, therapies frequently underperform in routine practice due to barriers in adoption, workflow integration, or sustained use. Implementation science addresses this gap by studying how evidence-based interventions are adopted, delivered, and maintained in real-world settings.8,9

Implementation science frameworks, such as the Consolidated Framework For Implementation Research (CFIR) and Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM), demonstrate that effectiveness alone does not determine success. Reach, adoption, fidelity, and sustainability all influence whether an intervention improves population health.10,11 Applying these principles within pharmaceutical development can strengthen trial design, improve launch readiness, and generate post-market insights into real-world therapy performance.

GLP-1 Therapies: A Real-World Example Of The Implementation Gap

GLP-1 receptor agonists illustrate how strong clinical efficacy does not automatically translate into consistent real-world impact. These therapies demonstrate substantial benefits for glycemic control, weight loss, and reductions in cardiometabolic risk in randomized trials.12,13 However, real-world studies show substantial early discontinuation and variability in adherence, influenced by tolerability, cost, treatment complexity, and patient preferences.14

Traditional adherence measures based on pharmacy refill data capture medication possession but rarely explain why patients discontinue therapy. Clinical narratives often document contextual drivers such as gastrointestinal side effects, financial concerns, or injection burden — factors that structured data sets alone cannot capture. Integrating structured utilization data with insights extracted from clinical documentation offers a more complete understanding of treatment persistence and disparities in adoption.

This example underscores a broader lesson: Real-world effectiveness depends not only on pharmacological innovation but also on the behavioral, social, and health system factors that shape implementation.

Integrating Real-World Evidence Across The Product Life Cycle

Organizations that derive sustained value from RWE integrate it across the product life cycle rather than deploying it only after launch. Early development can use RWD to characterize treatment pathways and identify potential adoption barriers. Pragmatic trial elements can enhance external validity during clinical testing. At launch, real-world insights can inform patient support programs and provider engagement strategies. Post-market monitoring can then evaluate persistence, disparities, and healthcare utilization outcomes to demonstrate long-term value.

This life cycle approach aligns with recommendations that continuous evidence generation improves both regulatory confidence and clinical adoption by ensuring that evidence evolves alongside therapeutic use.15

What Distinguishes Organizations That Successfully Translate RWE Into Impact

Organizations that consistently convert RWE into strategic advantage operate at a higher level of organizational maturity. Their differentiator is not simply access to data or analytic sophistication; it is how systematically RWE is embedded into decision-making across the product life cycle.

A useful way to conceptualize this evolution is through a Real-World Evidence Maturity Model, which reflects four progressive stages:

1. Reactive/Ad Hoc

RWE analyses are commissioned episodically, often in response to regulatory questions, payer challenges, or competitive pressures. Data use is fragmented, and analytic efforts are not connected across functions. Evidence is generated but not institutionalized.

2. Structured/Predefined Plans

Organizations begin developing formal RWE plans aligned with regulatory or market access milestones. Governance processes emerge, and analytic standards improve. However, RWE is still largely downstream, supporting launch or reimbursement rather than shaping development strategy.

3. Life Cycle-Integrated

At this stage, RWE is incorporated throughout development, launch, and post-market phases. Analytic questions are defined early, aligned with clinical endpoints, regulatory strategy, and value demonstration. Insights inform trial design, comparator selection, patient segmentation, and implementation planning. Cross-functional literacy improves, enabling shared interpretation of findings.

4. Institutionalized/Learning Health Organization

RWE becomes embedded within infrastructure, culture, and continuous learning loops. Data assets, analytic frameworks, and governance models are reusable across therapeutic areas. Evidence informs iterative refinement of patient support programs, digital engagement strategies, and value-based care initiatives. Feedback from post-market performance shapes future development decisions. RWE is no longer a deliverable; it is an operating capability.

Organizations that operate in the latter two stages share several defining characteristics. They cultivate shared methodological literacy across clinical development, regulatory, medical affairs, and commercial teams. They align analytic questions with operational decisions early rather than retrospectively seeking supportive analyses. They establish scalable governance structures that enable insights to accumulate across programs rather than being re-created for each product.

This capability-driven approach transforms RWE from a compliance obligation into a strategic engine for clinical development, launch optimization, life cycle management, and long-term value creation.

The Next Phase Of RWE Maturity

The pharmaceutical industry’s first wave of RWE investment focused on acquiring data assets. The second wave, which is ongoing, emphasizes advanced analytics, AI, and signal detection. The next phase likely will include organizational integration.

Future leaders in RWE will prioritize:

  • Building internal capability to interpret and operationalize evidence
  • Linking analytic outputs directly to implementation realities within health systems
  • Anticipating barriers to adoption before launch
  • Designing development programs informed by real-world patient complexity

The strategic advantage will not come from more data alone but from tighter integration between evidence generation and real-world execution.

Organizations that reach institutional maturity in the generation and use of RWE will be positioned to design more efficient clinical trials, support regulatory submissions with greater confidence, accelerate time to market, accelerate therapy uptake, and demonstrate sustained value to payers and health systems. In this model, RWE becomes not simply evidence of performance but a mechanism for continuous learning and improvement across the entire product life cycle — from development and regulatory decision-making to implementation in real-world clinical practice.

From Evidence Generation To Evidence Enablement

The future of RWE lies not simply in producing more analyses but in enabling better decisions. Evidence must move beyond publication toward practical influence, shaping provider behavior, patient engagement, formulary strategy, and healthcare delivery. Companies that build this full pathway from data to insight to implementation will ensure their innovations achieve meaningful real-world impact.

In this evolving environment, competitive advantage will belong not to those with the largest data sets but to those with the strongest capability to translate evidence into practice.

Several practical steps can accelerate this transition:

1. Embed RWE earlier in the development strategy.

Require that RWE questions be scoped alongside clinical development plans, not after pivotal trials conclude. Ensure that evidence planning informs comparator selection, endpoint strategy, and real-world feasibility assumptions.

2. Establish cross-functional evidence governance.

Create formal structures that connect clinical development, medical affairs, regulatory, health economics, and commercial teams around shared evidence priorities. Shared methodological literacy reduces friction and accelerates translation.

3. Close the loop between post-market evidence and development.

Institutionalize feedback mechanisms that directly inform life cycle management and future pipeline decisions through post-launch utilization patterns, adherence data, and real-world outcomes.

4. Tie RWE to implementation strategy.

Pair evidence generation with explicit plans for uptake, provider education, digital engagement, payer communication, and system-level adoption pathways. Evidence without implementation planning limits impact.

The shift from evidence generation to evidence enablement is ultimately a cultural evolution. Organizations that treat RWE as an operational capability, rather than a reporting function, will be positioned to design more efficient trials, accelerate therapy adoption, strengthen payer negotiations, and demonstrate sustained value across the healthcare ecosystem.

In the next phase of competition, the differentiator will not be who has more data but who can consistently convert evidence into action.

References:

  1. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-World Evidence — What Is It and What Can It Tell Us? N Engl J Med. 2016;375(23):2293-2297. doi:10.1056/NEJMsb1609216
  2. Concato J, Corrigan-Curay J. Real-World Evidence — Where Are We Now? N Engl J Med. 2022;386(18):1680-1682. doi:10.1056/NEJMp2200089
  3. Makady A, Van Veelen A, Jonsson P, et al. Using Real-World Data in Health Technology Assessment (HTA) Practice: A Comparative Study of Five HTA Agencies. PharmacoEconomics. 2018;36(3):359-368. doi:10.1007/s40273-017-0596-z
  4. Murphy LA, Akehurst R, Cunningham D, De Pouvourville G, Solà-Morales O. Real-world evidence to support health technology assessment and payer decision making: is it now or never? Int J Technol Assess Health Care. 2025;41(1):e20. doi:10.1017/S0266462325000145
  5. Frieden TR. Evidence for Health Decision Making — Beyond Randomized, Controlled Trials. Drazen JM, Harrington DP, McMurray JJV, Ware JH, Woodcock J, eds. N Engl J Med. 2017;377(5):465-475. doi:10.1056/NEJMra1614394
  6. Eichler H, Pignatti F, Schwarzer‐Daum B, et al. Randomized Controlled Trials Versus Real World Evidence: Neither Magic Nor Myth. Clin Pharma and Therapeutics. 2021;109(5):1212-1218. doi:10.1002/cpt.2083
  7. Bachinger M, Jankowski MA, Kesselheim AS, Krüger N. Real-World Evidence in Drug Approvals at the European Medicines Agency. JAMA Netw Open. 2025;8(11):e2542041. doi:10.1001/jamanetworkopen.2025.42041
  8. Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implementation Sci. 2022;17(1):75. doi:10.1186/s13012-022-01245-0
  9. Olson M (Skip), Zullig LL, Geest SD. How pharma can amplify product value with implementation science. J Comp Eff Res. 2024;13(10):e240076. doi:10.57264/cer-2024-0076
  10. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322-1327. doi:10.2105/AJPH.89.9.1322
  11. Glasgow RE, Harden SM, Gaglio B, et al. RE-AIM Planning and Evaluation Framework: Adapting to New Science and Practice With a 20-Year Review. Front Public Health. 2019;7:64. doi:10.3389/fpubh.2019.00064
  12. Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016;375(4):311-322. doi:10.1056/NEJMoa1603827
  13. Wilding JPH, Batterham RL, Calanna S, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384(11):989-1002. doi:10.1056/NEJMoa2032183
  14. Rodriguez PJ, Zhang V, Gratzl S, et al. Discontinuation and Reinitiation of Dual-Labeled GLP-1 Receptor Agonists Among US Adults With Overweight or Obesity. JAMA Netw Open. 2025;8(1):e2457349. doi:10.1001/jamanetworkopen.2024.57349
  15. Grueger J, Srikant V. Guidance for an Effective Approach to Integrated Evidence Planning in a Dynamic World. Clin Pharma and Therapeutics. 2025;117(4):920-926. doi:10.1002/cpt.3556

Disclosures:

The views expressed are those of the authors and do not necessarily represent those of Duke University or affiliated partners.

Hayden Bosworth reports research funding through his institution from BeBetter Therapeutics, Boehringer Ingelheim, Esperion, Improved Patient Outcomes, Merck, NHLBI, Novo Nordisk, Otsuka, Sanofi, Veterans Administration, Elton John Foundation, Hilton foundation, and Pfizer. He also provides consulting services for Boehringer Ingelheim, Esperion, Novartis, Sanofi, Vidya, Walmart, Webmed, Janssen, Rxrepius. He was also on the board of directors of Preventric Diagnostics.

Steven Grambow reports receiving consulting fees from Gilead Sciences and WCG Consulting for service on data monitoring committees.

About The Authors:

Hayden B. Bosworth, Ph.D., is a professor of population health sciences, medicine, psychiatry, and nursing at Duke University and deputy director of the Durham VA ADAPT Center of Innovation. He specializes in implementation science, pragmatic trials, and real-world evidence and is a cofounder of Luminate Insights. Luminate Insights provides customized clinical research skill and educational programs and is part of i-Cubed™, the center for clinical research innovation, powered by the Duke Clinical Research Institute. 



Steven Grambow, Ph.D., is an associate professor and associate chair of education in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. He serves as director of the Clinical Research Training Program (CRTP), Duke’s flagship degree-granting program for clinical and translational research education, and as co-director of the Workforce Development Pillar of the Duke Clinical and Translational Science Institute (CTSI). He is also a cofounder of Luminate Insights. Luminate Insights provides customized clinical research skill and educational programs and is part of i-Cubed™, the center for clinical research innovation, powered by the Duke Clinical Research Institute. 


Drew Narayan, MS, MBA, is an entrepreneur-in-residence at i-Cubed™ and a leader within Luminate Insights, focusing on strategy, partnerships, and corporate education initiatives connecting academic expertise with pharmaceutical innovation. Luminate Insights provides customized clinical research skill and educational programs and is part of i-Cubed™, the center for clinical research innovation, powered by the Duke Clinical Research Institute.