Unlocking Insights: The Importance and Use Cases of Real-World Data in Life Sciences

Written by Teyfik Agac

The ability to generate, analyse and access data has become increasingly vital. Life science companies invest vast amounts of effort and resources to generate data and identify valuable insights. Real-world data (RWD) plays an increasingly important role in this process. RWD encompasses information collected from various sources outside of traditional clinical trials, reflecting patient experiences in real-world settings. It offers valuable insights into treatment patterns, disease progression, and general patient outcomes.

Whilst having played a role in post market surveillance, RWD continues to gain traction and importance in other areas including clinical research and development, healthcare decision making and many more of the life sciences industry in recent times.

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Understanding Real-World Data

Covering medical data generated and gathered outside of clinical trials creates a highly diversified field of data falling under Real-World Data (RWD). This data is sourced from various sources, including electronic health records (EHRs), claims and billing data, patient registries, wearable devices, and mobile health applications. The field encompasses a wide range of information, including patient demographics, medical history, treatment patterns, and health outcomes.

Unlike data collected in controlled clinical settings, real-world data is collected in routine clinical practice. This reflects the diversity and complexity of patient populations and their healthcare experiences. Compared to other data and data sources, this enables real-world data to provide more comprehensive views of treatment performance in real-world settings and how patient outcomes are impacted over time.

Generating Insights: from Real-World Data to Real-World Evidence

In order to gain valuable insights, real-world evidence (RWE) must be extracted from real-world data, which is a critical process in the life sciences industry. Real-world evidence refers to the clinical evidence related to the use and potential benefits or risks of a medical product derived from the analysis of real-world data. Through rigorous analysis and interpretation of this data, real-world evidence is used to inform clinical research, optimize study design and support healthcare decision-making. Real-world evidence plays a critical role in regulatory decision-making, post-market surveillance and healthcare policy formulation to ultimately improve patient outcomes and drive innovation in healthcare.

Unlocking Insights: The Importance and Use Cases of Real-World Data in Life Sciences

Real-world data has become an invaluable resource for researchers, healthcare providers, regulators, pharmaceutical companies and patients. The use of real-world data as a source of insights and a reflection of real-world treatment and patient behavior can impact a variety of areas in the life sciences industry. Its importance can be seen in the following areas:

  • Information for clinical research and drug development:

    Real-world data can be used to complement data from traditional clinical trials by providing insights into the efficacy, safety and adherence of treatments under real-world conditions. By analyzing real-world data, researchers can potentially identify unmet medical needs, optimize trial design and accelerate the development of new therapies.

  • Improving patient cases and outcomes:

    Healthcare providers can use real-world data to make informed decisions about treatment options, patient management and resource allocation. In addition, real-world data enables analysis of best practices, monitoring of disease progression and evaluation of the long-term impact of treatments on patient outcomes. By using real-world data, the quality and efficiency of patient care by healthcare providers can be improved, leading to better outcomes and experiences for patients.

  • Support for regulatory decision-making:

    Regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are increasingly relying on real-world data to make decisions. Real-world data can provide valuable insights into the safety and efficacy of medical products under real-world conditions and help regulators make more informed decisions about product approval and post-market surveillance. Especially in rare disease cases and research, real-world data plays an important role as it is difficult to collect enough data for regulatory decision-making.

  • Promotion of value-based healthcare:

    By helping healthcare systems and payers measure and improve the quality, efficiency and cost-effectiveness of care, Real-World data plays a critical role in value-based healthcare initiatives. Considering Real-World data, opportunities to improve care delivery, reduction of costs and optimization of patient outcomes can be achieved.

Use Cases of Real-World Data in Life Sciences

A multitude of examples are available that demonstrate the importance of RWD in post market surveillance. Yet further value can be generated from RWD or RWE by including the data in clinical trials for drug approval. Examples are as follows:

  • Prograf (Tacrolimus):
    Received approval by the FDA in 2021 based on a non-interventional study proving effectiveness based on RWE for use in combination with further immunosuppressant drugs to prevent organ rejection in adult and paediatric patients.
  • Blincyto (Blinatumomab):
    Approved by the FDA in 2014 for the treatment of Philadelphia chromosome-negative relapsed or refractory B-cell precursor acute lymphoblastic leukemia (ALL). The condition was classified as orphan disease by the FDA, thus regulatory agencies accepted historical data as control group for the clinical trial data (historical RWD).
  • Ibrance (Palbociclib):
    Formerly only approved for usage in female breast cancer cases but classified as an orphan disease in males by regulatory agencies. Received approval for usage in male patients by the FDA in 2019 supported by the data generated by the trials in females for the initial approval.

These examples only give a small insight into the various areas of application for RWD in the real world. More and more new cases involving RWD and RWE can be seen throughout the industry.

Challenges with Real-World data in Life Science

Collecting and analyzing real-world data (RWD) in pharmaceutical drug development presents several significant challenges. The heterogeneity of RWD, sourced from electronic health records, insurance claims, and patient registries, leads to inconsistencies in data quality and format, necessitating advanced data harmonization techniques. Additionally, maintaining patient data privacy and ensuring regulatory compliance add layers of complexity, given the stringent laws governing health information use and sharing. The lack of controlled environments in real-world settings introduces confounding variables that can obscure causal relationships. Moreover, the vast volume of RWD demands sophisticated analytical tools and methods to extract meaningful insights. Continuous validation of findings is also required to ensure generalizability and applicability to broader patient populations, which is both resource-intensive and time-consuming.

Despite standards, best practices and guidelines being available, the diverse sources and formats of RWD demand a combination of multiple disciplines to be successful. Domain and technical expertise, a fundamental overview of the regulatory landscape and a clear understanding of data standardization are crucial to work reliably with RWD and RWE.

Conclusion

With the ongoing digital transformation in the life sciences, the importance of data is continuously increasing. RWD and the resulting RWE are possible solutions for this data requirement.

The ongoing efforts of all relevant stakeholders like big pharma, regulatory agencies and their data standards and guidelines, patients that use smart devices for self-monitoring and software providers developing simple, accessible applications for patients clearly show that the awareness of how much potential lies within RWD and RWE is present throughout the whole life sciences industry.

Due to the strong regulations present in the life sciences industry, robust data governance and standardization are crucial, and real-world data (RWD) is no exception. By implementing standardized data models, terminology, and exchange protocols such as those provided by OMOP, HL7, and SNOMED CT, stakeholders can ensure data quality, consistency, and interoperability. Additionally, effective data governance frameworks can be applied to further enhance the reliability and integrity of RWD, enabling the extraction of actionable real-world evidence.
To achieve the best possible outcome, we focus on not only one of the listed aspects of real-world data in practice but cover the overall lifecycle of real-world data and evidence. The diverse knowledge network at wega enables us to support pharma companies from conceptualization, throughout data standardization and governance up to data analysis and decision making.