Real-world data (RWD) and real-world evidence (RWE) are playing an increasingly important role in pharma. The exponential increase in electronic data combined with the availability of advanced analytics tools has made RWE an indispensable tool for assessing the performance of drugs and biologics.
RWE in pharma – Reflecting the realities of the real world
Health regulators around the world are using RWE to evaluate authorized drugs and guide regulatory decisions. Insights from RWE are used to develop guidelines and design clinical trials to test novel treatment options. RWE is also being used to support coverage decisions.
Real word data supplements data from RCTs and provides critical information reflecting the realities of the real world. However, the diverse nature and sheer volume of the data presents several challenges.
Real world data – What are the challenges?
Most RWD is generated as disparate data sets containing a wide variety of structured, or unstructured information. Additionally, the data may be procured from diverse sources (wearables, registries, electronic health records) and stored across disconnected silos. It might be challenging to integrate and transform such data sets to generate useful insights. The lack of consistent data quality and the long cycle time to gain insights are additional problems.
The advent of advanced RWE analytics
Advanced analytics and digital tools are being increasingly used to tackle the current data challenges and gain actionable insights from real-world data.
Advanced RWE analytics uses data science, bioinformatics, and machine learning. Predictive models, natural language processing, robotic process automation, probabilistic causal models, and ML algorithms are some ways to gain meaningful insights from rich data sets.
These methods sharply contrast the conventional analytical techniques which use descriptive analyses and matching methods.
How are pharma companies leveraging advanced RWE analytics?
With the availability of advanced RWE analytics, the significance of real-world data for pharma companies has tremendously increased.
With these latest techniques, companies can
- Enhance the R&D process
- Analyze diverse patient cohorts – Gain critical actionable insights from an array of patient characteristics
- Draw vital conclusions pertaining to drug performance
- Evaluate differential pharmacology patterns at a subpopulation level
- Generate hypotheses at scale across several therapies, comparisons, and endpoints
How can Bioviser help
Adopting advanced RWE analytics is critical to improve patient access and ensure product success. Given the technical and regulatory challenges associated with RWE generation, it’s critical that companies collaborate with partners that are well-versed with the process.
At Bioviser, our advanced RWE solutions are designed to ensure you have the right evidence at the right time for every phase of clinical development, across a product’s development cycle. With advanced analytical techniques, we can help you analyze, understand and navigate the growing ecosystem of clinically rich data. Our global team of experts can assist you with custom RWE solutions to fit your research needs.
By combining clinically rich data, advanced analytics, and expert knowledge, Bioviser can help you accelerate the time from data to impactful insights.
References
- Khosla S, Tepie MF, Nagy MJ, et al. The Alignment of Real-World Evidence and Digital Health: Realising the Opportunity. Ther Innov Regul Sci. 2021;55(4):889-898.
- Guo C, Ashrafian H, Ghafur S, Fontana G, Gardner C, Prime M. Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches. NPJ Digit Med. 2020;3:110.
- Zou KH, Li JZ, Imperato J, et al. Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications. J Multidiscip Healthc. 2020;13:671-679.
