Real-world data and evidence: FAQs

Real-world evidence is evidence obtained from real-world data. These are observational data, generated during routine clinical practice, obtained outside the context of tightly controlled randomized controlled trials. Real-world evidence is generated by analyzing data which are stored in electronic health records, electronic medical records, medical claims or billing activities databases, registries, patient-generated data and mobile devices, amongst other sources.

Real-world data are derived from a number of sources associated with outcomes in heterogeneous patient populations in real-world settings. Examples of real-world data sources include claims data, observational cohort studies, electronic and medical health records and national registries. Real-world data can also be generated from patient-reported outcomes and app-based questionnaires, such as through analysis of data obtained from wearables and patient surveys.

Real-world evidence and real-world data are being increasingly employed by various healthcare stakeholders.

Physicians and providers are interested in the use of real-world evidence for the assessment of patients’ treatment adherences, monitoring the safety of newly approved interventions and assessing adverse events associated with these. This real-world evidence can also inform stakeholders as to whether certain interventions are more effective in specific patient subgroups.

Payers employ real-world evidence, in the form of claims and billing data, to evaluate the cost–effectiveness of interventions, to help make drug coverage decisions and improve the affordability of healthcare.

Real-world evidence is also employed in regulatory decision-making, the post-marketing surveillance of drugs and medical devices, and in the development of guidelines and decision support tools for clinical practice.

Randomized controlled trials are considered the gold standard for determining whether treatments or interventions actually work. Relatively small patient populations – that represent only a fraction of a disease population – are recruited to participate in randomized controlled trials and the data derived from these clinical trials are often used to acquire regulatory approval for new drugs or interventions and make these readily available for patients.

By contrast, real-world data depict patients’ experiences in their real-world environments, outside of tightly controlled, unnatural clinical trial settings. Real-world evidence studies provide insights concerning whether a treatment does work, for real people in real-world environments, accounting for their other indications and medications, and the results pertain to a much larger, heterogenous patient population.

Following a previous project in which the results of 30 randomized controlled trials were replicated, there is an ongoing initiative to employ real-world evidence to predict the results of seven Phase IV trials. Termed ‘RCT DUPLICATE‘, this study uses the Aetion Evidence Platform™ (NY, USA) and represents the first time researchers will attempt to predict the results of trials that have not reached conclusion.

In the USA in 2016, the 21^st Century Cures Act – often simply referred to as the Cures Act – was passed. This Act modified the US FDA drug approvals process and allowed companies to incorporate real-world evidence, patient input and claims data into their drug approval applications, rather than clinical trial data only.

Electronic health records are systematized collections of patients’ and populations’ electronically stored health information, in a digital format. Electronic health records are often used as sources of real-world data.

This distinction is very nuanced: an electronic health record comprises all of a patients’ health record, across all medical service-proving institutions. By contrast, an electronic medical record is the single database of an individual’s information within one institution.

Comparative effectiveness research concerns the conduct, and/or synthesis, of research directly comparing different benefits and harms of alternative interventions or strategies to prevent, diagnose, treat and monitor a specified health condition in routine clinical practice (i.e. the real-world setting). Comparative effectiveness research includes both primary data collection and secondary analyses – such as systematic literature reviews, meta-analyses and economic evaluations.