Real-World Evidence: Key Challenges and Privacy Concerns

26 December 2023
Katerina Krinitsyna, Head of Business Development
Real-World Evidence: Key Challenges and Privacy Concerns

Real-World Evidence: There is a growing trend of using real-world data (RWD) in clinical research. It holds immense potential in driving groundbreaking discoveries and revolutionizing the healthcare and medical field. Clinical study approaches are shifting to real-world data instead of solely depending on controlled clinical trials. This gives a wider perspective to healthcare systems so they can assess the true effects of medical treatments in real-world settings.

However, the use of real-world data is not without its challenges. We must cope with issues like fluctuating data quality, security concerns, and patient consent, while remaining vigilant about data privacy. In this article we will discuss the significance of using real-world data and identify the key issues that come with it.

Real-World Evidence: Why is it important to use in clinical research?

RWD is a treasure trove of information regarding the safety and efficacy of medical products, routinely collected outside the rigid protocols of clinical trials. It originates from diverse sources, including patient registries, medical health records, insurance claims, and mobile technologies, including sensors and electronic wearables. This data aids in identifying unmet medical needs in specific regions, uncovering the natural progression of diseases, and assessing their overall impact. 

Real-world evidence (RWE) is derived through the analysis of RWD to determine the usage, benefits, and risks associated with a medical product. Healthcare professionals, biotech pioneers, and pharmaceutical companies are actively leveraging Real-World Data to enhance health outcomes.

RWD complements traditional clinical trials, and both the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have demonstrated confidence in their potential. The FDA has created a framework to assess the utilization of RWD in evaluating new indications for already approved drugs.

RWD facilitates the design of flexible study parameters, including considerations such as sample size, selection criteria, and study endpoints, ultimately paving the way for more effective and adaptable clinical research.

What are the challenges of Real-World Evidence in clinical trials?

There are several challenges associated with the use of real-world data in clinical trials. These challenges include:

  • Quality of data: RWD is often messy and incomplete. The data can have errors and biases. This affects the reliability and insights derived from it. Think about a real-world dataset for a new medication in which some patient records contain inconsistent or incorrect data. When assessing the medication's efficacy and safety, these inaccuracies can lead to skewed conclusions.

In such a scenario, cross-stakeholder collaboration and public transparency can come to the rescue. This increases the availability of relevant and high-quality RWD for more use in cases. For example, in the case of RWD quality findings, especially critical ones, publishing data quality assessment results identified by vendors can make a big contribution. This allows for the creation of a robust unified RWD ecosystem. This is obviously a time-consuming solution, but the return in the long run is worthwhile. 

  • Privacy concerns: Patient confidentiality is an ethical and legal obligation as RWD includes sensitive medical data.

Therefore, a high level of attention must be paid to anonymization and confidentiality protection, so that the risk of identifying any individual is minimal. Proper use of innovative digital tools, regular audits, risk assessments and implementation of end-to-end encryption for electronic channels are a must in order for data providers to protect personal information in the digital world.  

  • High volume of data: The surge in data volume is both a blessing and a burden. High data volume makes it complicated to manage and derive analysis from it. 

Although a high volume of data offer a richer dataset for evaluation, it also creates a deluge of data. Advanced data management systems are thus required to navigate and extract meaningful information efficiently. Researchers should also leverage data management platforms [LK4], as they can aid in structuring unstructured [LK5] RWD. Such platforms include advanced analytical tools. This structured data can then be analyzed and interpreted.

  • Unstructured data: RWD is often unstructured and not in any standard format. This makes it challenging to use in clinical trials.

RWD is often gathered from various sources, each of which uses a different data format and structure. This lack of standardization makes data integration and analysis within clinical trials more difficult.  Nevertheless, this issue can be overcome by the implementation of AI-based natural language processing (NLP) tools that provide constant data enrichment. Comprehensive training programs on digital security for medical staff in clinics and research sites also dramatically improve the completeness of primary data.

What is the future of real-world data?

Real-world evidence (RWE) is gaining significant importance in clinical trial design and patient recruitment, contributing to increased patient access, enhanced diversity, and greater trial efficiency. The rapid growth of decentralized clinical trial tools and virtual sites is a testament to the expanding adoption of RWE. Pharmaceutical companies are increasingly turning to RWE not only for trial design and recruitment, but also to support financial discussions for drug reimbursement and to explore additional applications for marketed therapies.

The evolution of Real-World Evidence points to its continued development into a comprehensive end-to-end capability. RWE will play an important role in shaping the design of clinical trials and observational studies, with a primary focus on developing novel treatment approaches.

It will remain an essential tool for demonstrating the safety and efficacy of healthcare products. Furthermore, RWE goes beyond these basic functions by providing a therapeutic context for well-informed decision-making. It is expected that its influence will extend to expanding label indications, potentially broadening the horizons of healthcare products and their applications.


In conclusion, the use of real-world data is expected to continuously grow, reshaping clinical research and healthcare practices. Continuous advancements in assessing real-world data and overcoming limitations will be instrumental in realizing the full potential of RWE in these transformative endeavors.

As we build a global network of healthcare organizations and data providers, Therapyte is at the forefront of advancing healthcare knowledge across the EU and CIS regions. 

At Therapyte, we are leading the charge in harnessing Real-World Evidence (RWE) to revolutionize clinical research and patient care. Our expertise and innovative AI algorithms drive the curation of data from millions of electronic health records, ensuring the highest quality and privacy standards. Reach out to us to learn more about the advantages of RWE for your clinical projects.