5 challenges you will need to overcome to obtain Real-World Data

Real-World Data (RWD) has become one of the biggest buzzwords in the Pharmaceutical and Medical Technology industry in 2017. Medicines are becoming more complex, powerful and expensive, and the regulatory authorities in both the US and EU are relying more heavily on RWD to build evidence. The FDA views data generated from real-world studies as a critical complement to randomised clinical trials (RCT) and the new Medical Device Regulations in the EU require Medical Technology companies to submit more RWD. These trends are leading to a growing recognition of the role that RWD plays in building evidence of treatment efficacy and assessing the impact on patient outcomes and cost-effectiveness. 

There are many ways of obtaining RWD. A large part of RWD comes from prospective studies. These include observational and non-interventional studies, registries, patient-reported outcomes, patient diaries and more. 

Conducting prospective studies requires a different approach to the one used in clinical trials. These are the top 5 challenges you will need to overcome to generate high-quality RWD efficiently and effectively:

1. RWD: too many sources. Where should I go?

2. Is it OK to use a different process for RWD?

3. Is my study representative of real-life?

4. How do I manage research-naïve sites?

5. Am I using the right tools?

1.     RWD: too many sources. Where should I go?
Real-world data can come from many different sources: any data generated outside of a randomised clinical trial is considered real-world data. When looking to fill the evidence gap, the question often is – where can I find this data quickly and cost-efficiently? Buying a database or access to a retrospective data set is quick and efficient but it does not always answer specific questions about treatment impact or outcomes and lacks patient data granularity.

2.    Is it OK to use a different process for RWD?
For people expert in the scientific rigour of a randomised controlled study, this can be an uncomfortable activity. When a study follows the doctor as they practice medicine, the research activity is very different from one which includes an investigational medicinal product and the nature of the data is equally different. Underestimating the importance of this leads to protocols that simply do not work for observational studies. Using the same SOPs as in clinical trials is both inefficient and costly, and generally results in poor data quality and frustration. Allowing Medical Affairs and Epidemiologists to set up different yet standard SOPs and processes that are fit-for-purpose for non-interventional studies has shown to be a successful strategy.

3.    Is my study representative of real-life?
In observational studies, we need to broaden the recruitment pool not only of patients but also of sites to make the study representative of the real world. One mistake to avoid is designing the study based on site or data availability. To truly collect rich RWD, it is important to capture it from a broad selection of sites with the right balance of physician segments, patient segments and provider types.

4.    How do I manage research-naïve sites?
In observational studies, we recruit sites often not used to doing clinical trials. This means that we will rely on data from healthcare professionals whose primary focus is the care of their patients and therefore have little time or interest in filling out forms. They are not paid very much for this activity and are not aware of the big picture of the data they are gathering. Therefore, the greatest success driver when working with research-naïve sites is to treat them as partners in science. We have found that engaging the site by giving value and making it as easy as possible for them greatly improves data quality and site retention.

5.    Am I using the right tools?
eClinical EDCs have become the norm in clinical trials for the past 15 years, replacing paper CRFs, thereby increasing speed and efficiency. However, most eClinical EDCs are neither designed nor optimised for observational studies. This means that research-naïve sites find the tool complicated to use. Feedback from Data Managers shows that eClinical EDCs lack the design flexibility to adapt to the variety of real-world studies. As a result, study managers wish for higher data quality, less missing data and higher engagement from sites. Using a fit-for-purpose platform that has been designed for real-world research addresses these concerns. Specialist technologies, which are created with the healthcare professional in mind and built on the principles of standard of care, will lead to increased data quality and greater efficiency.

To learn more about real-world studies and how to obtain real-world data efficiently, visit us.

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