Driving Pharma Forward: A Data-Centric Approach to Real World Evidence

Driving Pharma Forward: A Data-Centric Approach to Real World Evidence

Posted on November 08, 2018 0 Comments

We recently attended the London-based event series, PharmaTec 2018, along with our partner WIPRO, who we have been working with to enable pharmaceutical companies to more efficiently access and understand data and speed new product development.

At the event, we discussed Real World Evidence (RWE), which aims to exploit data derived from multiple sources outside of typical clinical research settings to improve the speed and quality of clinical studies. This includes electronic medical records (EMRs), claims and billing data, product and disease registries and data gathered by personal devices and health applications. This new approach will not only accelerate product development but ultimately drive the pharma industry forward.

Market access is one of the biggest challenges facing the pharma industry today. In fact, Deloitte estimates that developing and gaining marketing approval for a new drug costs life-science companies a staggering US $2 billion.

With lengthening drug development timelines, increasing failure rates and skyrocketing development and market approval costs, the incorporation of RWE has enormous potential to transform the pharma industry and reverse some of its most pressing issues.

The premise behind RWE is a simple one, richer data will yield better healthcare decisions and better care. According to The Network in Health Innovation, “robust RWE will not only tap increasing volumes of data, but weave together different sources of data, such as clinical data, genomic data and socioeconomic data, to yield a better picture of individual patient characteristics and improve medicine’s ability to treat individual patient needs.”

Many companies and industry bodies are looking to RWE to transform the healthcare industry, but there are still issues with adoption. As many other industries have experienced, collecting, managing and storing data is not as straightforward as it sounds. Companies across the life-sciences sector need to learn the lessons of other industries or risk having their very own Big Data problems.

The value RWE offers is that it comes from the real world, granting pharma companies a window into places they cannot usually see. However, utilising RWE means employing data that has its own vocabulary, format and context that these companies didn’t create and have no control over. These challenges are daunting but not insurmountable.

Simplifying Multi-Structured Data Integration

The main issue with using this data is access and control. Often when large amounts of data is collected, it is stored in individual systems, meaning you have separate databases for patient records, claims and billing data, clinical notes and more. To make sense of RWE and obtain the critical insights it enables, companies need to be able to draw all this information out of these silos and bring it together in one place for analysis. Therefore, for RWE development to be successful, organisations must begin with a database that can support the integration and harmonisation of multi-structured data.

Thankfully the technology exists that can meet these challenges—NoSQL database platforms. Using this technology, life-sciences organisations can integrate vast and disparate datasets together and harmonise it all for actionable analysis, whilst also keeping it secure. For data scientists, this means having the ability to use advanced semantics capabilities to search through a unified dataset to access greater insights. Whether it’s adverse event reporting, data discovery or competitive intelligence, a solution such as this can advance enterprise objectives around real world evidence—improving time to market for new products and reducing the overall cost of IT infrastructure.

As an example of this, our partnership with WIPRO leveraged the MarkLogic® Data Hub to enable life-science organisations to create a metadata-driven, semantically enriched operational platform to support an innovative RWE data strategy. Through our partnership, we were able to facilitate the harmonisation of information from diverse sources, transformation of information into evidence that improves patient outcomes and through machine learning and semantics, facilitate increased insight into the patterns, connections and relationships that inform drug development and commercialisation.

For life-science companies, taking a data-centric approach to real world evidence means having the ability to demonstrate the clinical and economic value of their products. With the improved understanding of the underlying causes of disease that this data provides, drug and medical manufacturers will be able to more rapidly provide new drugs and devices to the public that result in higher quality of care and improved patient outcomes.

More Information

Learn how MarkLogic’s database platform enables organisations to realise faster time-to-insight in their real world evidence development efforts:

  • Learn how to Do More with All of Your Data in Less Time — Data-wrangling work prior to complex data integration is a time-intensive practice exacerbating the already lengthy and expensive processes of product development and FDA approval. Learn how an operational data hub platform empowers pharmaceutical companies to efficiently integrate disconnected and variable data for faster time to analysis and insight.
  • See the interactive story, Escape the Matrix and its companion eBook.
  • Read our eBook: Introducing the Operational Data Hub.

Jen Shorten

Jennifer Shorten is Technical Delivery Architect for Consulting Services, EMEA. She joined the company in 2014 after six years working on MarkLogic implementations with some of the world’s leading media and publishing companies. In her seventeen years in the industry, Jen has helped customers meet their strategic goals by overcoming data and content challenges. Jen has a particular interest in semantics and has had the opportunity to lead the EMEA consulting team on some of the most advanced semantics implementations for MarkLogic customers globally.

Originally from New York City, Jennifer holds a Bachelor’s Degree in Biopsychology from Hampshire College and an MSc in Technology Management from NYU Polytechnic School of Engineering.


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