Medical research is designed to answer difficult questions under carefully controlled conditions. Everyday healthcare is rarely quite so controlled.
Patients have different lifestyles, multiple health conditions, varying levels of treatment adherence and individual circumstances that cannot always be fully represented within a traditional clinical trial. This creates an important challenge: understanding how findings from research translate into routine care.
Real-world data is helping researchers look at that question from another perspective.
What Do We Mean by Real-World Data?
Real-world data refers to information relating to patient health or healthcare delivery that is routinely collected from a variety of sources. According to the FDA, these sources can include electronic health records, medical claims, product and disease registries and digital health technologies.
When this information is analysed to produce clinical evidence about the use, potential benefits or risks of a medical product, it can generate what is known as real-world evidence.
The distinction matters. Data on its own is simply information. It becomes useful evidence when appropriate research methods are used to analyse it and answer meaningful questions.
Looking Beyond Controlled Research Environments
Traditional clinical trials remain fundamental to medical research. Their controlled structure allows researchers to investigate treatments while reducing many of the variables that could complicate the results.
However, patients encountered in routine healthcare can be considerably more diverse than participants in a particular trial.
Someone may have several medical conditions simultaneously, for example. They might take multiple medications or have difficulty following a treatment schedule. Age, access to healthcare and other factors can also affect outcomes.
Real-world research can therefore provide another layer of understanding by examining what happens when treatments are used across broader patient populations and everyday clinical environments.
Understanding Treatments Across Their Lifecycle
The evidence surrounding a medical product does not stop being relevant once its initial clinical development programme has finished.
Researchers, healthcare organisations and regulators may continue asking questions about safety, effectiveness and treatment patterns as products are used more widely.
Real-world evidence can contribute to this ongoing process. The FDA, for example, uses real-world data and evidence in regulatory decision-making across the medical product lifecycle, including assessments relating to safety and effectiveness.
This creates opportunities to learn from healthcare as it is actually being delivered rather than viewing research and routine care as completely separate environments.
Finding Answers When Traditional Research Is Difficult
Some research questions are particularly difficult to investigate using conventional approaches alone.
Rare diseases provide an obvious example. When relatively few patients have a particular condition, recruiting large numbers of participants for research may be challenging. Similar difficulties can arise when researchers need to investigate specific patient subgroups or long-term outcomes.
Access to suitable Real-World Evidence Solutions can help research teams design studies and analyse real-world information to investigate questions across different stages of clinical development.
This does not mean replacing clinical trials. Instead, the opportunity lies in determining when different sources of evidence can complement one another.
Making Research More Representative
Another potential benefit concerns representation.
Clinical trials use eligibility criteria for important scientific and safety reasons. However, those criteria can sometimes mean the population participating in research differs from the wider population that eventually receives a treatment.
Real-world data can provide information about larger and potentially more varied groups of patients. Researchers may be able to examine how outcomes differ according to patient characteristics, treatment patterns or healthcare settings.
That can provide useful context when considering how findings from controlled research relate to routine clinical practice.
Data Quality Still Matters
The fact that data comes from the real world does not automatically make it reliable or suitable for research.
Electronic health records, insurance claims and registries were not necessarily created to answer a particular research question. Information may be incomplete, recorded differently between organisations or lack important clinical detail.
The FDA has specifically highlighted challenges involving the accuracy, completeness and consistency of real-world information, alongside considerations around privacy and appropriate data access.
Researchers therefore have to consider whether a data source is suitable for the question being asked and whether the methods used to analyse it are sufficiently rigorous.
David Prior
David Prior is the editor of Today News, responsible for the overall editorial strategy. He is an NCTJ-qualified journalist with over 20 years’ experience, and is also editor of the award-winning hyperlocal news title Altrincham Today. His LinkedIn profile is here.
