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Insight

The New Currency in Healthcare: Real-World Data

 

The bustle around real-world data (RWD) has been increasing in recent years. This is hardly surprising, since RWD can provide new insights on the benefit-risk ratio throughout a medicinal product’s life cycle, or update our current understanding of medical practices.

This work is licensed under a Creative Commons Attribution 4.0 International License ©www.tOrange.us

Creative Commons Attribution 4.0 International License ©www.tOrange.us

With the insights derived from RWD, clinicians can make more appropriate treatment decisions, pharma and health-tech companies can optimize post-marketing studies, and regulators can understand the real impact of new treatments. Decision makers have also noticed the importance of RWD. According to Niko Andre,Head of Global Medical Affairs at Roche:

“Today, regulators and payers increasingly assess whether a new drug is adding significant benefit over an established standard of care, and whether the price of the drug correlates with a positive health impact for the patient.”

What is driving this trend? Until recently, randomized clinical trials (RCTs)have been the gold standard for evaluating the efficacy and safety of new treatments. Although RCTs still hold an irreplaceable position in the evaluation of new therapies, they do have several drawbacks, such as a limited number of pre-selected patients, and standardized study protocols. Accordingly, RCTs do not accurately represent what happens when drugs or devices are used in large populations in actual clinical practice.

In contrast, RWD can offer information from sources that are not included in RCTs, including data gathered directly from medical records in a regular clinical setting, or information collected through national registries and population surveys. In the real-world clinical practice there are no study protocols and patients are not filtered through strict exclusion or inclusion criteria.

Furthermore, valuable information can also be collected from subpopulations including children, pregnant women, and patients using several drugs. Analysis and synthesis of RWD can lead to real-world evidence (RWE),which can be used to answer questions related to compliance, long-term efficacy, and cost insights – just to mention a few. Importantly, technological advances have made the generation and collection of health-related data is easier than ever, and new players in the field, such as Apple,have also noticed the value of RWD.

Here are a few insights into how RWD could be used in pharma and health-technology:

  • In contrast to conventional clinical trials, real-world data can provide information on up to millions of patientsworldwide and answer ad hoc questionson issues such as long-term safety, medication adherence, and response duration in heterogenous populations.
  • Actual health outcomes as well as the effect of multiple treatments can be assessed in detail, which not only help patients but also help providers better define the real clinical value of new therapies,and further update current treatment guidelines.
  • RWD can also help physicians compare alternative treatment approacheswith multiple criteria and find the optimal therapy for a specific subgroup of patients.
  • Total cost of carecan be monitored through RWD, which can guide decision makers in evaluating the real impact of a treatment. In addition, regional data collection can assist in making local reimbursement decisions.
  • If utilized properly, RWD can help pharmaceutical companies design better clinical trialswith appropriate patient selection schema. RWD can also add value to all stages of the product life cycle.

Although RWD has already fulfilled some of its promises, there are important questions to be answered in the near future. Not all of the available data are high quality and reliable, thus it is important to develop good research practices for collecting and reporting RWD.

In addition, increases in transparency would accelerate the usability of existing records as well as future data collection. The ISPOR Real-World Task Reportalready discussed these issues in 2006, concluding that “It is critical that policy makers recognize the benefits, limitations, and methodological challenges in using RW data, and the need to consider carefully the costs and benefits of different forms of data collection in different situations.”

In Europe, the ENCePP networkis actively working on increasing transparency in pharmacoepidemiology and pharmacovigilance. The European Medicines Agency (EMA) coordinates this service, and the network has generated resources that facilitate high quality and transparent research practices as well as provide a platform for collaboration.

 

Creative Commons Attribution 4.0 International License ©www.tOrange.us

Creative Commons Attribution 4.0 International License ©www.tOrange.us

Enormous amounts of healthcare data have been around for a while and now we finally have the tools and methods to process and analyse it. But are the pharmaceutical and health-tech companies ready to utilize it?

To get the most out of RWD, you need to combine the right data with the appropriate tools. Furthermore, you also need the expertise to turn the analysis into actionable insights that can engage stakeholders and other interest groups appropriately. However, this often requires close collaboration across healthcare sectors, which can turn out to be challenging.

Fortunately, experts like MedEngine can help you to get the most out of RWD – from tailored analysis of existing data to anything from initial data collection design to actual data gathering, analysis, and reporting. It is time to redeem the promises of RWD, and we are happy to help you to do that!

Anna Grönholm

Anna Grönholm

Anna received her PhD in immunology from the University of Tampere in 2015. During her doctoral studies, she worked on a wide range of projects varying from basic immunology to diagnostic biomarkers, both in Finland and in the US. In addition to her biomedical training, Anna has a few extra feathers in her cap, having also studied communication, data journalism, and marketing. Anna is passionate about learning new things—whether they are related to new treatments for inflammatory diseases or innovative ways to utilize data. Apart from science, she enjoys mountain hikes, traveling and photography, or relaxing at home with her family and some good books. Anna is an honorary member of MedEngine’s rabbit owner division, although she currently doesn’t have rabbits of her own.