Source: Cave, A. , Kurz, X. and Arlett, P. (2019), Real‐World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe. Clin. Pharmacol. Ther., 106: 36-39. doi:10.1002/cpt.1426

Real-world data (RWD) offers the possibility to derive novel insights on the use and performance of medicines in everyday clinical use, complementing rather than competing with evidence from randomized control trials. While Europe is rich in healthcare data, its heterogeneous nature brings operational, technical, and methodological challenges. Cave et al., (2019) present a number of potential solutions to address the full spectrum of regulatory use cases and emphasize the importance of early planning of data collection

There is increasing interest in the use of real‐world data (RWD) to support regulatory decision making across the product life cycle. Key sources of RWD are electronic health records, claims data, prescription data, and patient registries. Increasingly incorporated into the definition is data from wearables, m‐health apps, and environmental data including data on social status, education, and other lifestyle factors. These latter data offer much promise to deliver a holistic picture of an individual’s health status but from a regulatory standpoint present substantial challenges in deriving actionable evidence. From the perspective of the European Medicines Agency (EMA), RWD are defined as “routinely collected data relating to a patient’s health status or the delivery of health care from a variety of sources other than traditional clinical trials.” We specifically exclude traditional clinical trials even if single arm but would incorporate data from pragmatic clinical trials if data were collected remotely through an electronic health record or other observational data source and solely under conditions of normal clinical care.1 Real‐world evidence (RWE) is then defined as the information derived from analysis of RWD, and it is the acceptability of this evidence for regulatory decision making in different use cases across the product life that has become the subject of intense debate

Conclusions

The digitization of health care and, increasingly, lifestyle data bring new opportunities to complement and enhance the data traditionally utilized in regulatory decision making. The hope is that this will improve the timeliness, accuracy, and relevance of decisions across the product life cycle. Defining the exact evidentiary standards of such RWE a priori is challenging as necessary standards will vary depending on the context within which the question is asked. Given the broad range of regulatory use cases, it seems clear that a one‐size‐fits‐all approach will not be sufficient; a hybrid approach to evidence generation will be required, depending on the question being asked and the context in which the derived evidence will be used, and early planning of the strengths and limitations of the possible approaches is required. However, whatever the approach, there is a need to address operational, technical, and methodological challenges in both designing, running, and assessing a study to enhance the quality of evidence generated and the consistency of regulatory decision making. Moreover, as more data sources become available and infrastructures are developed to enable access, there is an urgent need to consider and plan for the data needs for the future. Standardizing and validating data retrospectively is expensive, time consuming, and potentially introduces errors and biases, and hence it is important to consider in advance the scope, depth, and quality of data that will be required to generate reliable evidence suitable for multiple regulatory use cases. This work requires effort from the multiple stakeholders who may potentially wish to utilize these data for decision making. With the combination of technological and scientific advances available today, there has never been a more opportune time to address this.

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