Frequently asked questions about Real World Evidence Solutions
Real‑world data (RWD) refers to health data collected through routine clinical care rather than controlled clinical trials. In Nordic countries, RWD is primarily sourced from national health registers, electronic health records, quality registers, prescription databases, and biobanks. These data are population‑based, longitudinal, and can be reliably linked across healthcare settings using unique personal identification numbers, making Nordic RWD particularly well suited for regulatory and health technology assessment (HTA) purposes.
Real‑world evidence (RWE) is the robust clinical and economic evidence generated by analyzing RWD using validated epidemiological and statistical methods. In regulatory and HTA settings, Nordic RWE is used to address evidence gaps that remain after clinical trials, such as long‑term effectiveness, safety in broader patient populations, treatment patterns, and healthcare resource utilization. It can also support post‑authorization safety studies, supplementary regulatory submissions, and joint clinical assessments.
In summary, Nordic RWD is the underlying data infrastructure, while Nordic RWE is the decision‑ready evidence derived from it. The strength of the Nordic model lies in its ability to produce high‑quality, generalizable evidence that is increasingly recognized by regulatory authorities and HTA bodies internationally. Accordingly, Nordic RWE is a critical complement to randomized clinical trials, helping to inform regulatory decisions, reimbursement evaluations, and value assessments throughout the product lifecycle.
RWE can support all the phases of a product’s lifecycle.
While randomized controlled trials (RCTs) are the foundation of drug development, designed to evaluate efficacy under controlled conditions, real‑world evidence (RWE) provides a complementary perspective across the product lifecycle. By leveraging real-world data, it enables assessment of how treatments are used and perform in routine clinical practice, including broader and more diverse patient populations. In the Nordics, this is typically achieved using national health registers and linked healthcare data, which support longitudinal follow-up and capture patient pathways across different care settings.
During drug development, RWE has been used to inform trial design, identify relevant patient subgroups, and understand disease burden or treatment pathways ahead of pivotal studies. When RCT data are limited, for example in rare diseases or small populations, RWE can also be used to support external or historical control arms and other innovative study designs.
After regulatory approval, the focus shifts. RWE is used to understand how the treatment performs in routine clinical practice, outside the controlled trial setting. This includes evaluating real‑world effectiveness, long‑term safety, treatment patterns, and use in broader and more representative patient populations. Post‑approval RWE also plays a key role in fulfilling regulatory commitments and informing reimbursement and health technology assessment decisions, where stakeholders often want evidence beyond what can be generated in pre‑approval trials.
For regulatory authorities such as the European Medicines Agency and the U.S. Food and Drug Administration, real-world evidence (RWE) is commonly used to support post‑approval activities. This includes post‑authorization safety studies and efficacy studies, monitoring long‑term effectiveness and safety, evaluating use in broader or under‑represented patient populations, and fulfilling regulatory commitments after launch. In specific circumstances, such as rare diseases, small populations, or label extensions, RWE can also contribute to assessments where traditional trials are not feasible or where additional RWE is needed.
Health technology assessment (HTA) bodies such as National Institute for Health and Care Excellence in the UK and Canadian Agency for Drugs and Technologies in Health in Canada use RWE in a slightly different way. They rely on RWE to assess how a treatment performs in routine clinical practice and to inform value‑based decisions. This often includes evaluation of real‑world comparators, treatment pathways, healthcare resource use, and longer‑term outcomes. RWE helps HTA bodies evaluate whether the benefits observed in trials translate into real‑world impact and whether a therapy justifies reimbursement at the proposed price.
Across both regulatory and HTA settings, RWE plays a key role in reducing uncertainty, supporting reassessments over time, and informing decisions as additional real-world data (RWD) become available. High‑quality data sources, such as Nordic health registers with full population coverage and long follow‑up, are particularly well suited to these purposes and are increasingly used in both regulatory evaluations and HTA submissions internationally.
With the introduction of the EU HTA Joint Clinical Assessment (JCA), the role of RWE is becoming even more important. JCAs aim to provide a shared clinical assessment at the EU level, while national bodies retain responsibility for pricing and reimbursement decisions. In this context, RWE is increasingly used alongside trial data to reduce uncertainty, supplement evidence on comparators and patient populations, and inform national interpretation and adaptation of JCA conclusions, particularly after launch, when additional RWD become available.
Real-world evidence (RWE) has been used in some form for many decades, but its role in drug development has evolved significantly over time. Early use of real-world data (RWD) was primarily focused on post‑marketing safety and pharmacovigilance, particularly from the mid‑20th century onwards, as regulators recognized the need to monitor medicines after approval using observational data.
From the early 2000s onwards, the use of RWE in drug development decision‑making expanded with the increasing availability of digital health data, national healthcare registers, and advances in epidemiological and statistical methods. Over the past 15-20 years, RWE has moved beyond safety monitoring to support a broader range of activities, including disease understanding, study design, and post‑approval evidence generation.
Today, RWE is used across the product lifecycle. During early development, it helps characterize disease epidemiology, treatment pathways, and unmet needs, supporting more informed and feasible trial design. After approval, RWE supports post-authorization safety studies, long‑term effectiveness assessments, and evidence generation required by regulators and health technology assessment bodies after launch.
Overall, RWE makes drug development more iterative and data‑driven. Rather than relying solely on clinical trials conducted at fixed points in time, evidence can continue to be generated as treatments are used in routine care. This supports faster, more targeted development decisions, better post‑approval oversight, and a stronger evidence base for regulatory and reimbursement decisions, especially in areas such as rare diseases, oncology, and chronic conditions.
AI is expected to change how real-world data (RWD) and real-world evidence (RWE) are generated, analyzed, and used, but it is not replacing established scientific methods or human involvement. Instead, its main impact is on scale, speed, and consistency.
At the data level, artificial intelligence (AI) can help handle the growing volume and complexity of RWD. Techniques such as natural language processing and machine learning enable more efficient extraction of relevant information from unstructured sources like clinical notes and free‑text fields, as well as improved linkage and harmonization of data across multiple sources.
At the analysis stage, AI can support faster data exploration and pattern recognition, helping researchers identify signals, patient subgroups, and treatment patterns that might be difficult to detect using traditional approaches alone. This is particularly valuable in large‑scale datasets such as national health registers, where AI can help prioritize analyses and improve the efficiency of study execution.
That said, regulatory and health technology assessment bodies are cautious. Agencies such as the European Medicines Agency, Food and Drug Administration, and National Institute for Health and Care Excellence emphasize that AI‑supported analyses must be transparent, reproducible, and clearly validated. “Black‑box” models without clear rationale are unlikely to be accepted for decision‑making on their own. Human oversight, a clear context of use, and robust study design are still essential for regulatory‑grade RWE.
Overall, AI is a powerful tool for RWD and RWE, improving efficiency and scalability, but scientific judgement and methodological rigor remain central to how RWE is assessed and used in regulatory and reimbursement decisions.
- (Apart from general description, we can highlight Nordic RWD)
- (and RWEaaS)
- (and EpiX as a multi-function platform for market research, Ad board, data visualization)
Ciencia Research supports real-world evidence (RWE) generation by combining deep experience in real-world data (RWD), flexible RWE delivery models, and technology-enabled insights.
A key part of our approach is access to Nordic ´RWD, which enables population-level analyses with long-term follow-up and rich clinical detail. Nordic registers cover entire populations and can be linked at the individual level using personal identity numbers, allowing data from hospital care, prescriptions and socioeconomic sources to be combined. This allows robust evidence generation on, for example, treatment outcomes, healthcare resource use, and indirect costs across real-world settings. Read more about RWD and our experience here.
Flexible RWE expertise, when you need it
We complement our data capabilities with a Real-World Evidence as a Service (RWEaaS) model. This gives pharma and biotech teams on-demand access to experienced RWE scientists. RWEaaS can support a specific study, contribute to an Integrated Evidence Generation Plan, or embed RWE expertise directly within internal teams for a defined period, working as part of the team without the need for permanent hiring. Read more about this service here(link).
Technology-enabled evidence generation with EpiX
In addition, we use EpiX, our AI-enabled evidence platform, to support data exploration, analysis, and communication. EpiX combines data storage, data visualization, and virtual advisory board capabilities, allowing teams to explore patient populations and validate findings with clinical experts in real time. Read more about this service here.
Nordic real-world data (RWD) provides a strong foundation for generating high-quality evidence to support clinical development, market access, and public health decision-making.
Nordic RWD is often considered among the most robust in the world, largely due to the structure of Nordic healthcare systems and a long tradition of systematic data collection. Countries like Sweden, Denmark, Norway, and Finland maintain nationwide health registers that cover entire populations and can be linked at the individual level using unique personal identity numbers.
This linkability is a major advantage, as it allows researchers to follow patients over time and across different parts of the healthcare system, including hospital care, prescriptions, and disease-specific quality registers. It also enables linkage to other national registers, making it possible to incorporate socioeconomic data, sick leave and social insurance data, link parents and children, and construct matched cohorts from the general population for comparative analyses.
In Sweden alone, there are more than 100 national quality registers and over 200 biobanks, providing rich clinical detail that is rarely available at scale elsewhere. In addition, laboratory data can often be extracted directly from hospital electronic health records. In countries such as Finland, centralized data lake solutions further enable access to structured lab and clinical data at scale.
Another key strength is data completeness and consistency. Because healthcare is largely publicly funded, most healthcare interactions are captured, reducing gaps commonly seen in RWD collected in other healthcare settings. For instance, insurance claims data, commonly used in other markets, is primarily collected for billing purposes and often lacks clinical depth.
These combined features make Nordic RWD particularly well-suited for a wide range of real-world evidence analyses, including survival and time-to-event analyses, studies of healthcare resource utilization and costs, long-term follow-up of treatment outcomes across large populations, and analyses of indirect costs, such as productivity loss and sick leave, which are often difficult to capture in other data settings.
Nordic real-world evidence (RWE) provides a robust and pragmatic complement to clinical trial in regulatory decision-making.
Nordic RWE supports regulatory decisions by generating population-level evidence that complements clinical trial data. Because Nordic countries maintain comprehensive, linkable health registers, researchers can assess treatment effectiveness, safety, and long-term outcomes across entire populations.
Regulatory agencies such as the European Medicines Agency and the U.S. Food and Drug Administration have expanded the use of RWE, particularly where randomized controlled trials are not feasible or where additional evidence is needed post-approval. Methods such as target trial emulation are increasingly applied to estimate treatment effects from real-world data by explicitly defining and emulating a trial design.
RWE is a key component of an Integrated Evidence Generation Plan, helping to address evidence gaps across the product lifecycle that are not covered in clinical trials. Nordic data are particularly valuable in this context because it allows long-term follow-up of patients and access to clearly-defined, population-based cohorts.
In practice, Nordic RWE is used to support regulatory submissions, inform label extensions, and generate evidence in settings where trial data is limited. It is also widely applied in post-authorization safety studies, long-term outcome studies, and evaluations in broader, more representative patient populations than those typically included in trials.
Nordic real-world evidence (RWE) helps translate clinical trial results into real-world value, supporting more informed market access decisions.
Nordic RWE supports market access by providing insights on how treatments perform in routine clinical practice, beyond the controlled setting of clinical trials. This includes evidence on effectiveness, safety, and healthcare resource use, all of which are relevant for reimbursement and pricing decisions.
Because Nordic real-world data (RWD) covers entire populations and enables long-term follow-up, it is well suited for demonstrating treatment value in real-world settings. This is particularly relevant for health technology assessment bodies and payers when evaluating whether a treatment should be reimbursed and under what conditions.
The ability to link healthcare data with socioeconomic and social insurance data further makes it possible to assess broader outcomes, such as indirect costs, productivity loss, and sick leave. This strengthens cost-effectiveness evaluations and the overall value story, especially in settings where societal perspective is considered.
In practice, Nordic RWE is used to support reimbursement submissions, inform pricing and negotiation discussions, and generate evidence for value-based agreements. It can also help address uncertainties that remain after clinical trials, for example by providing data on long-term outcomes or clinically-relevant patient subgroups.
Real-World Evidence as a Service (RWEaaS) provides a practical and scalable way to access experienced real-world evidence (RWE) support, helping teams move faster while maintaining quality and flexibility.
RWEaaS is a flexible model where companies access RWE expertise on demand. It allows organizations to bring in experienced support for a specific study or embed our experienced researchers within internal teams, functioning as part of the team for a defined period, similar to a full-time role, but without the need for permanent hiring.
Compared to traditional RWE consulting, which is often project-based with a fixed scope and timeline, RWEaaS is designed to be adaptable. Support can scale up or down depending on the stage of the project or evolving evidence needs, allowing rapid alignment with shifting priorities.
This model is particularly useful when internal teams face capacity constraints, during hiring gaps, or when specific expertise is needed, for example in Nordic register studies, regulatory-grade RWE generation, or advanced study designs.
RWEaaS can also support the development and execution of an Integrated Evidence Generation Plan, from identifying and prioritizing evidence gaps to translating them into feasible study designs and delivering studies aligned with regulatory and market access requirements across the product lifecycle.
In practice, RWEaaS can support the full evidence generation process, from early planning and strategy through analysis and publication. It complements internal teams, helping reduce bottlenecks and maintain continuity across projects without the need for permanent hires.
EpiX is an AI-enabled evidence platform developed by Ciencia Research that brings together real-world data (RWD), analytics, and expert input in one place. It provides access to insights from longitudinal RWD, integrated scientific sources such as PubMed and ClinicalTrials.gov, and tools to manage and explore complex datasets efficiently.
The platform supports evidence generation by combining data access with interactive analysis and visualization, where teams can explore patient populations, treatment patterns and outcomes. EpiX also enables input from clinical experts through virtual advisory panels, helping connect data insights with real-world clinical context.
In practice, EpiX can support key steps in evidence generation strategies, from early data exploration and study feasibility to ongoing analysis and communication of results. By bringing data, analytics, and expert insights together, it helps teams move faster, refine research questions, and make more informed decisions across the product lifecycle.