The Use of Real-World Data and Evidence in Clinical Trials

In recent years, there has been increasing use of real-world data (RWD) and real-world evidence (RWE) as a complementary approach to traditional randomized controlled trials. RWD, as defined by the FDA, is data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources such as electronic health records (EHR), claims data, and patient registries. RWE is clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.¹ According to the FDA, “advances in the availability and analysis of RWD have increased the potential for generating robust RWE to support FDA regulatory decisions.”²

Historically, the FDA has utilized RWE primarily for post-marketing surveillance to monitor and evaluate the safety of drug products. However, the adoption of RWD and RWE have expanded significantly within the pharmaceutical industry. RWD can enhance the efficiency of clinical research through observational data beyond controlled environments. Additionally, RWD can facilitate faster accrual, including rare indications, and contribute to reduced research costs.³

Despite these advantages, both industry stakeholders and regulatory authorities, such as the FDA and EMA, are cautious of integrating RWD into clinical trial designs due to concerns about the reliability of RWE, as they are subject to various types of measurement errors and biases.⁴

While previous benchmarking studies on RWD and RWE usage in drug development have been conducted, there are few published articles detailing therapeutic area-specific uses of RWD and RWE. Tufts Center for the Study of Drug Development (Tufts CSDD) and Verana Health, a digital health organization focused on developing large databases through the use of artificial intelligence and machine learning, collaborated to examine the use of RWD and RWE on clinical trials across pharmaceutical companies and contract research organizations (CROs) in the following therapeutic areas: oncology, ophthalmology, neurology, dermatology, and urology. This study focuses on current use cases of RWD and RWE and high impact areas of RWD in the pharmaceutical field. Other areas of investigation are challenges around using RWD and industry ideas on future directions.

Methods

Tufts CSDD conducted 30-45-minute in-depth interviews between October and November 2025 to examine the impact of RWD and RWE in clinical trials across five therapeutic areas. A total of 23 semi-structured interviews were conducted with 25 drug development professionals, representing 17 unique pharmaceutical companies and one CRO. Interviews were audio-recorded, transcribed, and reviewed for accuracy. Thematic data coding and analysis were completed in Microsoft Excel. The interviews focused on key areas including adoption and usage, budget allocation and resources, partnerships, regulatory trends, and challenges to the use of RWD and RWE. Other areas examined included key performance indicators (KPIs), return on investment (ROI), and on identifying specific use cases.

Participant characteristics

Interviewees are primarily from large pharmaceutical companies, with the greatest proportion having expertise in oncology (Table 1). In terms of functional areas, interviewees are nearly evenly split among research and development, and health economics and outcomes research, although some interviewees reported working across multiple therapeutic areas and functional areas.

Adoption of RWD: General use cases

Organizations reported using RWD to support many aspects of research and development. RWD is used to aid decision-making throughout the development process, including during early-stage discovery and identification of target areas, pre-approval interventional clinical trials, and treatment guideline development. Organizations also use RWD within epidemiology for background research on the indication of interest, including characterizing social determinants of health, disease history and characteristics, disease progression, burden of disease, and current standard of care.

Sponsors also apply RWD and RWE to the design, optimization, and feasibility testing of clinical trials. Examples include using RWD for site identification and selection, developing outreach tactics, making trial operation decisions, and pressure testing for trial feasibility. In terms of design, organizations have also used RWD to help determine eligibility criteria, validate targets, estimate treatment effects, and select endpoints.

RWD is also helpful within the realm of evidence generation as an external comparator to help contextualize the study and position the model. For example, sponsors reported using RWD to justify a study control arm and run hybrid trials consisting of a smaller randomized control with a RWD external control arm. In innovative cases, sponsors have used RWE to form a synthetic data arm.

Organizations are also using RWD within commercialization and evaluation. Sponsors have used RWD to determine market information, including disease and drug characterization by subpopulations; track real-world effectiveness and treatment patterns; and make decisions around market positioning, price valuation, and access decisions.

Sponsors report use of RWD to support adherence to regulatory requirements, and some have submitted RWD as additional evidence in regulatory submissions, as well as to defend trial design and confirm the feasibility of trial changes from regulators. In addition, organizations utilize RWD to assist with safety monitoring, including adverse event monitoring, contextualizing safety signals, and identifying early safety signals in dose changes.

Adoption of RWD: Use cases by therapeutic area

Organizations also shared insights on specific use cases to support functions across therapeutic areas.

Oncology

Oncology is a fast-moving, quickly changing field, with a strong evidence base of positive RWD and RWE use cases. For example, one organization used global RWD and real-world outcomes in first and second-line settings to inform clinical trial decisions, such as inclusion-exclusion criteria, sample population of interest, and sample size. Other use cases leverage RWE as pivotal evidence to obtain accelerated FDA approval for a drug in the rare disease space, with RWE as additional evidence in label expansion studies and RWD as a benchmarking tool for future indirect treatment comparisons.

Ophthalmology

Organizations working in ophthalmology shared that product development is generally more concerned with functional outcomes, for example, how products are improving or protecting vision, visual acuity, or function. Specific use cases of RWD and RWE in this space include conducting RWD analysis to understand the disease burden with and without the company’s product during the pre-approval stage, then analyzing product utilization and treatment patterns in the post-approval stage and using RWD to identify enrichment strategies in specific patient subgroups, informing regulatory strategy in several countries.

Dermatology

Organizations in dermatology reported using RWD to address additional post-marketing requirements and determine the unmet burden of disease. In one case example shared, RWD was used to support additional post-marketing requirements researching safety outcomes, including maternal outcomes and risk of malignancy, for the dermatological indication.

Neurology

Within the neurology space, organizations have leveraged RWE to conduct quality checks and proactively identify safety signals, as well as to support age and sex matching in clinical trials.

Urology

RWD use is still in the piloting phase in urological clinical research. One sponsor reported that researchers are focused on evidence-generation, gathering biomarkers, medical affairs statistics, and RWE to help with future planned studies.

Areas of greatest impact

Organizations are assessing the impact of RWD and RWE across trial monitoring and optimization. RWD is used to inform study design, including the identification of efficacy and safety endpoints, optimization of sample size, and estimation of the probability of trial success (Table 2). These areas can lead to reduced costs and more efficiency during trial execution. Additional KPIs include development timeline acceleration and reduction in overall study cycle time.

Site and patient population identification, as well as rapid access to data has helped enable faster decision-making, reduce trial burden, and accelerated response times to address regulatory inquiries. RWD has also guided rapid responses to research questions by utilizing the existing data (rather than conducting new clinical trials) often leading to decreasing patient burden.

Interviewees also noted that market gains were a result of the ability to produce additional evidence to support the label. One use case included a pediatric application of RWD and RWE, specifically a trial with an experimental arm and an external control arm. The study did not meet its primary endpoint of superiority, but as a benefit, the pediatric population did not need to be randomized to the control arm. As a result, less participants were exposed to the drug, and the trial results were published.

Beyond trial design, many KPIs are concentrated in decision-making, evidence generation, and regulatory support activities. These include metrics related to dataset utilization, such as the frequency and cost benefit analysis across development activities, and time to evidence readiness for health technology assessment bodies and regulatory authorities. Citations, encores of presentations at various conferences, reviews and engagement are softer KPI measures organizations have used to track impact of RWD and RWE. Publications in research-focused journals have supported the inclusion of their product on the formulary.

Though interviewees do not have a precise or quantitative measure of ROI, 10 out of 14 interviewees anticipate an increase in ROI over the next one to two years. This is primarily due to the increasing interest from stakeholders for RWD and RWE, which reinforces the value proposition of products. AI and technology use will further drive innovation and reduce cost and timelines, better integrating RWE into regulatory, access, and development decisions. While regulatory support has grown, many interviewees expect the ROI to remain stable.

Challenges and potential solutions

Data and vendors

Organizations reported that data quality, completeness, and continuity were key challenges. As RWD is often collected for medical reimbursement purposes, using data built for different purposes to provide insights can pose difficulties. Several organizations identified key areas of challenge including the lack of linkage between real-world datasets and erroneous or messy data collection. Also cited were the inability to capture variables needed for RWD analysis (for example, mutation-specific biomarkers or response rates) or to follow the entire patient journey given various treatment settings. Sponsors also reported on the hurdles to establishing partnerships, including building trust in vendor data quality, addressing duplicate dataset purchases within the company, and challenges that emerge with forming new partnerships including identifying initial contacts.

Interviewees discussed solutions related to data and vendor challenges, including normalization of data structure, cleaning, and curation and closer alignment of trial endpoints with real-world practices. Others noted that developing vendor qualification processes can assist with evaluation during the vetting phase.

Application and value

Organizations also highlighted barriers to RWD application and value definition. These include applying RWD to research and clinical trial operations, as well as defining and conveying the ROI of using RWD. In clinical research, determining where RWD is most applicable is critical. Additional challenges are having differing endpoints in real-world datasets and clinical research and a lack of data generalizability due to population or geography-based datasets.

As RWD is often retrospective and drawn from existing data, sponsors reported a time delay in applying findings to clinical trial operations especially for patient recruitment. Furthermore, organizations noted difficulties with quantifying the economic impact and benefits of RWD investments, as well as communicating the value of RWD to internal and external audiences. Some organizations noted that Integrated Evidence Generation, a strategy that involves bringing all stakeholders together and identifying data gaps early, may help address RWD applicability issues.

Change management and workforce

Organizations observed several roadblocks in implementing RWD including company silos and workforce adoption and proposed some solutions. Specific challenges include lack of cross-functional collaboration and resource sharing and the importance of developing collaboration and alignment among departments and functions.

Also, the existence of function-dependent RWD and RWE ownership and a lack of widespread acceptance for RWE in decision-making are other areas that organizations are addressing through hiring and developing staff with critical skills and investing in training. Several organizations noted that addressing the need for strong leadership to guide the direction of RWD and RWE was also essential.

Future directions

The industry’s perception of RWE has evolved significantly with broader acceptance and adoption across the drug development landscape. Organizations anticipate more formal and integrated use of RWD throughout the drug development timeline.

In early development and trial design, RWD is expected to play a larger role in informing site selection and assessing trial feasibility. Some examples include using claims data and EHR data to model where eligible patient populations are concentrated, estimate enrollment rates, and evaluate local standards of care. Organizations are also exploring more ways to leverage RWD in interventional trials, such as evaluating treatment-effect modifiers and assessing transportability across diverse populations.

Rather than relying on fragmented datasets, sponsors are increasingly investing in longitudinal patient-level data to map the patient journey and track shifts in standard of care, supporting precision medicine strategies. By generating comparative effectiveness evidence in routine practice, sponsors can provide data showing superior outcomes, improved safety profiles, or reduced healthcare utilization relative to existing therapies. Cross-national RWD analyses could illuminate variations in practice patterns and inform strategies to drive broader adoption where clinical benefit is demonstrated.

Looking ahead, organizations envision incorporating more complete and integrated datasets into the drug development process. Application of artificial intelligence and machine learning (AI/ML) is well suited to extract and standardized insights from raw, unstructured clinical notes across systems, as well as incorporating data from wearable technology. ML components help map patient journeys and assess relevance to treatment or indication to advance precision medicine.

The anticipated benefits include reduced trial startup timelines, improved geographic diversity, and mitigation of recruitment risks prior to trial launch. As organizations continue to learn new ways to use RWD in interventional trials, these approaches are expected to accelerate development decisions and enhance overall efficiency.

Conclusion

This qualitative study is limited to the perspectives of the stakeholders; however, the participation of various large and mid-sized organizations demonstrates wider adoption and improves applicability. Despite the limitation, the study provides valuable insights on how the industry perceives the evolving role of RWD and RWE in clinical development—RWD and RWE is becoming an increasingly important component in clinical development with the growing support among drug development professionals, specifically in decision-making; trial design and optimization; and commercialization and real-world efficacy. With challenges to data quality, applicability, and change management, organizations highlighted areas where further guidance and regulatory alignment may refine adoption. Other suggestions which emerged through the interviews are being implemented to combat these challenges:

• Normalization of data structure, cleaning and curation;
• Developing a process that helps FDA uphold its data quality protections within the constraints of patient privacy and protection policies;
• Integrated Evidence Generation: Bringing stakeholders together and identifying data gaps early on;
• Cross-functional collaboration and alignment between departments; and
• Workforce development including education and training as well as strong leadership to guide adoption and usage of RWD and evidence generation.

Future research considerations should further evaluate the integration of AI/ML with RWD/RWE in clinical development. AI/ML has the potential to analyze data from wearable devices and mobile health applications, and provide real-time insights on patient health, treatment effectiveness, and adverse effects. Recent FDA draft guidance⁵ on AI/ML provides more clarity with RWD or RWE and will enhance the efficiency of clinical research.

As datasets become more complete, the use of RWE for rare diseases can be broadened especially where populations are small and many challenges exist with finding patients to enroll in traditional trials. RWE can play a larger role in this area and represent enormous potential for treatment of disease.⁶

Another area to consider is the use of pragmatic trials which provide a bridge between clinical trials and RWD.⁷ Pragmatic trials are beneficial as they offer potential cost savings yet have methodological rigor. They also are advantageous in terms of broad generalizability to populations and clinical settings.⁸ Pragmatic trials offer insights into real-world settings and wide applicability to various clinical settings.

As RWD and RWE use in clinical trials continue to evolve, RWD can fill critical gaps in drug development and safety. It is important for sponsors and companies along the drug development continuum to continue evaluating where RWD and RWE can be applied most effectively. Normalization of data structure, Integrated Evidence Generation, and increased cross-functional collaboration provide opportunities to improve RWD and RWE applications.

This study was funded by Verana Health

About the authors

Hana Do, MPH is a research analyst at Tufts CSDD

Victoria Zhang, MPH is a research analyst at Tufts CSDD

Mary Jo Lamberti, PhD is research associate professor and director of sponsored research at Tufts CSDD

Craig Morgan, NZCS, PMP, PCA, MBA is vice president of marketing at Verana Health

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