Real World Evidence – And Other Ways of Changing Healthcare

Real World Evidence – And Other Ways of Changing Healthcare

As the healthcare landscape grows more complex and data-driven, so too must the methods used to generate evidence. Traditionally, randomized controlled trials (RCTs) have been held as the gold standard, valued for minimizing bias and demonstrating causality. However, their design is based on highly selective patient populations and tightly controlled conditions, which often limits how well their findings apply to everyday clinical practice. Real-world evidence (RWE) has emerged as a powerful complement to RCTs, offering insights from routine care.

By leveraging various data sources, RWE helps capture the complexities of everyday clinical practice that traditional trials may overlook. However, in areas where data is sparse or evolving, expert insight becomes essential. Structured consensus methodologies like the Delphi method can be used to fill these evidence gaps and support high-quality decision-making.

Rethinking Evidence: The Shift Towards Real-World Data

Regulators, payers, and clinicians are placing greater emphasis on evidence that reflects the realities of routine care. This shift has driven the growing use of real-world data (RWD), which is clinical data collected outside of traditional trials, such as from electronic health records, patient-reported outcomes, and insurance claims. When analyzed appropriately, RWD is transformed into real world evidence, which informs treatment decisions, safety in broader patient populations, and cost-effectiveness, amongst numerous other applications (Figure 1).

Support for real world evidence is increasing among regulatory bodies such as the Medicines and Healthcare products Regulatory Agency (MHRA), the European Medicines Agency (EMA), and the U.S. Food and Drug Administration (FDA) ^1–3. An IQVIA analysis of 16,515 health technology assessment (HTA) reports across 83 HTA bodies in 33 countries showed that the proportion of records incorporating RWE increased from 6% in 2011 to 39% in 2021 ⁴. Highlighting its use in regulatory decision-making, the FDA approved the use of Prograf (tacrolimus) for preventing organ rejection in patients receiving a lung transplant based on RWE ⁵. In addition, current initiatives such as the MHRA’s 2024–2027 Data Strategy, and the Real-World Evidence Scientific Dialogue Programme, aim to enhance the use of RWD and RWE to accelerate regulatory decisions, improve quality and transparency, support earlier patient access to innovative therapies, and offer confidential guidance to researchers and developers on generating and using real world evidence in regulatory submissions ^6,7. Together, these developments reflect a strategic shift toward embedding real world evidence across healthcare decision-making and highlight its growing recognition as a vital component in regulatory frameworks.

Figure 1: Applications of RWE in healthcare

Despite its promise, real world evidence is not without limitations. Challenges such as incomplete datasets, inconsistent coding, and lack of standardization in collection and reporting methodologies can affect data quality and interpretation ⁸. Moreover, real world evidence may not always provide clear answers when evidence is limited, uncertain, or absent. This is where structured consensus techniques come into play and fill the gap.

Structured Consensus: Bridging the Evidence Gap

Structured consensus methods provide a systematic way to generate credible guidance. They are designed to gather the collective expertise of diverse stakeholders, such as clinicians, researchers, patients, and policymakers, to ensure that decisions are informed by a broad range of perspectives and knowledge. There are many consensus-led evidence techniques (Figure 2), but among the most widely used are the Delphi method, which gathers expert opinion through iterative surveys to reach agreement, and the Nominal Group Technique, which involves structured in-person discussions to prioritize issues ⁹. These methods help address evidence gaps by integrating expert judgment with available data to inform decision-making in a transparent and reproducible manner.

Figure 2: Consensus-led evidence techniques that can be used in healthcare

The Delphi Method: Structured, Credible Expert Insight

The Delphi method is highly effective and regularly used in healthcare. It has been refined and adopted across healthcare, policy, and research to generate expert guidance in a wide range of contexts. The Delphi method involves multiple rounds of anonymous surveys with a panel of carefully selected experts. Each round builds upon the responses of the previous one, with feedback provided to participants between rounds to promote reflection, clarification, and convergence towards consensus.

Key characteristics of a Delphi study include:

• Anonymity: Reduces the influence of dominant personalities
• Iteration: Allows experts to reconsider opinions as the group evolves
• Controlled feedback: Enables transparency and refinement
• Quantitative and qualitative outputs: Generates both consensus thresholds and explanatory insights

Delphi studies are especially useful for:

• Defining clinical best practices or care pathways
• Informing treatment guidelines
• Identifying and addressing unmet needs
• Identifying ideal patient populations
• Defining disease definitions or diagnostic criteria

Because Delphi outputs are evidence-informed and expert-led, they are valuable in regulatory submissions, HTA, and strategic planning.

Consensus as a Complement to RWE and RCTs

Structured consensus methods are not only valuable when evidence is limited, uncertain, or absent; they also serve as a complement to RWD and RCTs. When used together, these approaches offer a more comprehensive, evidence-based understanding of clinical reality.

An example of this is demonstrated in a recent case study by Triducive: RCTs established the efficacy of a new biologic therapy for psoriasis in a defined population; real world evidence demonstrated its effectiveness in broader, routine care; and a Delphi study was used to define the ideal patient profile and treatment algorithm based on expert consensus. The result? A richer, multi-dimensional evidence base that supports better-informed and actionable decision-making across clinical, regulatory, and commercial settings.

Figure 3: Evidence pipeline supporting therapy evaluation and decision-making

By systematically capturing expert insight, Delphi studies bridge the gap between data and clinical experience, transforming expert opinion into structured, credible guidance to get better decisions actioned.

Ready to Employ the Delphi Method and Create Positive Change?

Triducive specializes in delivering consensus-led evidence that gets published and supports change. With over a decade of experience and more than 50 peer-reviewed publications, we’ve helped transform evidence into action for healthcare systems worldwide.

If you’re exploring new ways to generate evidence and inform meaningful change, Delphi consensus could be the missing piece in your strategy.

Get in touch today with our expert team to learn how we can help with your project.

References:

1. Real-World Evidence | FDA, https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence (accessed 29 May 2025).
2. Real-world evidence | European Medicines Agency (EMA), https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/real-world-evidence (accessed 27 May 2025).
3. MHRA guidance on the use of real-world data in clinical studies to support regulatory decisions – GOV.UK, https://www.gov.uk/government/publications/mhra-guidance-on-the-use-of-real-world-data-in-clinical-studies-to-support-regulatory-decisions?utm_source=chatgpt.com (accessed 27 May 2025).
4. IQVIA Institute for Human Data Science. Impact of RWE on HTA Decision-making., https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/impact-of-rwe-on-hta-decision-making (2022, accessed 29 May 2025).
5. FDA approves new use of transplant drug based on real-world evidence | FDA, https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-new-use-transplant-drug-based-real-world-evidence (accessed 29 May 2025).
6. MHRA Real-World Evidence Scientific Dialogue Programme – GOV.UK, https://www.gov.uk/government/publications/mhra-real-world-evidence-scientific-dialogue-programme/mhra-real-world-evidence-scientific-dialogue-programme (accessed 29 May 2025).
7. MHRA Data Strategy 2024 -2027 – GOV.UK, https://www.gov.uk/government/publications/mhra-data-strategy-2024-2027/mhra-data-strategy-2024-2027?utm_source=chatgpt.com#executive-summary (accessed 29 May 2025).
8. Zisis K, Pavi E, Geitona M, et al. Real-world data: a comprehensive literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process. Journal of Pharmacy and Pharmaceutical Sciences 2024; 27: 12302.
9. Humphrey-Murto S, Varpio L, Wood TJ, et al. The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review. Academic Medicine 2017; 92: 1491–1498.