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Real-Word Data (RWD) and Real-World Evidence (RWE) are acronyms that have been thrown around the industry for some time. As healthcare, life sciences companies and regulators look at the potential research opportunities that stem from RWD/RWE, these topics and the discussion of their potential impact on clinical research are becoming increasingly popular.  

FDA defines real-world data (RWD) as data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources and real-world evidence (RWE) as the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.1 The FDA issued draft guidance titled, Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products in September 2021. The guidance discusses collection and validation of data sources, the validation of study design elements, and maintaining data quality in a study-specific data set. The agency is accepting public comments on the draft through November 29, 2021.  

RWD can come from a variety of sources including pharmacy and health insurance claims/databases and billing records, electronic healthcare records, pragmatic clinical trials, and product/disease registries. RWD can also be patient generated through social media channels. 

The Impact of RWD/RWE 

The use of RWD/RWE offers a much broader view of a patient population. This helps researchers understand how a product will affect a more diverse population, their experience with the disease state and the therapy, and the overall patient journey. Use of RWD and RWE will likely result in more efficient product development strategies that minimize the number of patients assigned to a less effective treatment arm within a clinical trial. 

However, RWD is only as good as the data source from where it originates. As with other areas of clinical research, data quality, data standards and governance, compliance, and costs impact how and where RWD/RWE will be used most effectively.  

As the demand for RWD/RWE increases, we will likely see additional benefits of complementing randomized controlled clinical trials with observational data from standard clinical practices. For clinical R&D teams, RWD/RWE have the potential to create numerous new data streams that require management, control, and compliance. Less than perfect real-world data may require additional quality assurance tasks to ensure its proper use and relevancy.  

RWE is a type of big data that will require powerful computing and analytics capabilities. It will require the flexibility to process and manage vast amounts of data coming from many different sources within varying data streams. Depending on the source and type of data, different categories of data will require their own ‘data journey’ to ensure control, compliance, and accuracy. To get the most form these large amounts of data, access and transparency will also be essential. 

Preparing for the Future 

As R&D teams assess their approaches to RWD/RWE, it will be important to understand the value of each and how they enhance the R&D effort. Upfront planning times may increase because data quality plans will have to address a wide range of data pathways. The adage, ‘garbage in – garbage out’ will be relevant when teams assess large data sets and look for ways to extract important insights.  

The consensus continues to be that the value of RWD/RWE outweighs the challenges of collecting, managing, processing, and extracting insights from such large datasets. It will require effort, but will provide significantly broader views of products, treatments, and devices that cannot be obtained in traditional clinical trials.  

As new data streams and new sources of data emerge, flexible platforms will be required to allow for integrations and enable research teams to modify procedures while maintaining control and compliance. The iMednet platform offers the flexibility required to support the opportunities and emerging requirements of clinical trials. Integrated modules enable global teams to access and analyze data anywhere in the world.