Real world evidence in medicine means evidence obtained from real world data, which are observational data obtained outside the context of randomized controlled trials and generated during routine clinical practice. In order to assess patient outcomes and to ensure that patients get treatment that is right for them, real world data needs to be utilized. RWE is generated by analyzing data which is stored in electronic health records, medical claims or billing activities databases, registries, patient-generated data, mobile devices, etc. It may be derived from retrospective or prospectiveobservational studies and observational registries. In the USA the 21st Century Cures Act required the FDA to expand the role of real world evidence. Real World Evidence comes into play when clinical trials cannot really account for the entire patient population of a particular disease. Patients suffering from comorbidities or belonging to a distant geographic region or age limit who did not participate in any clinical trial may not respond to the treatment in question as expected. RWE provides answers to these problems and also to analyze effects of drugs over a longer period of time. Pharmaceutical companies and Health Insurance Payers study RWE to understand patient pathways to deliver appropriate care for appropriate individuals and to minimize their own financial risk by investing on drugs that work for patients.
Data quality
In order to use real world data to generate evidence, data must be of sufficient quality. Kahnet al. define data quality as consisting of three components: conformance ; completeness ; and plausibility.
Fitness for purpose
Similarly to having sufficient data quality, the real world data must be fit for purpose. An RWD resource can be fit for addressing some questions, but not others. For example, a dataset that lacks mother-to-baby links may not be appropriate to address drug risk for fetus but can be used for questions for drug safety in patients taking epilepsy treatment. Since data quality can be evaluated outside a particular purpose, fitness for purpose is evaluated separate from data quality and is not included in the concept of data quality.
