Over the past few decades, R&D divisions in the pharmaceutical industry have had to grapple with increasing costs. One increasingly prevalent way to reduce costs in R&D is to extract more from the post-commercial value chain via real-world data.
Unlike data collected in clinical trials, which is extremely focused and limited by design, the real-world data pharma companies seek out is highly varied in nature and collected by many different kinds of healthcare entities, such as private practices, insurance providers, mobile data, and other sources. Real-world data may be both structured and unstructured and sometimes data records are missing key elements. However, real-world data can expand upon clinical trial data by bringing in data from both normal clinical practice and everyday life.
Real-world data has been around as long as the healthcare industry itself, but advances in digital and analytics technologies are allowing for it to be used more than ever before. This kind of data can help pharmaceutical researchers better understand how unique patient attributes and behaviors impact the outcomes of medical treatments. Real-world data combined with modern analytic approaches can allow for the better prediction of disease progression, patient reactions to a medication, or adverse effects. From a business point of view, real-world data can extract more value from R&D investments and accelerate the time to market for new medications.
To access real-world data, pharma companies require direct access to data silos throughout the healthcare industry. Access to real-world data is still considered quite limited, and one of the greatest opportunities for the pharma industry is broader access to many data partners for analyses. Greater access would be of particular benefit to the artificial intelligence and machine learning technologies commonly used in modern R&D.
Turning Real-World Data into Real-World Evidence
When pharmaceutical companies can access real-world data and analyze it, they translate it into what is called real-world evidence.
While real-world data is a tool used in research, real-world evidence is the currency of analysis, and it is closely tied to implementation. Using real-world evidence, pharma companies can make more informed decisions. This information is derived from real-world data but not exclusively, as it may include information from clinical trials and other sources. In recent years, real-world data has become an increasingly viable source of real-world evidence in pharma thanks to advances in healthcare analytics.
Previous approaches in healthcare analytics used descriptive analysis to sort patients and conventional matching strategies to compare patient groups with similar qualities. More recent approaches use machine learning, predictive models, and unsupervised algorithms to unlock deeper insights from complex datasets.
These more modern approaches allow pharmaceutical companies to leverage thousands of patient attributes to better understand outcomes and extract insights on drug performance at a more granular level. Companies can also use these techniques to develop predictive models and formulate hypotheses at scale for multiple therapies.
Real-world evidence is already being used to fuel innovations in large pharmaceutical companies. For example, Pfizer was able to use data from electronic medical records to get approval for a new breast cancer drug called Ibrance. Another example is AstraZeneca using real-world evidence data to illustrate the real-world effectiveness of Farxiga, a medication used to treat diabetes. Other use cases include predicting outcomes of an ongoing phase IV trial for a cardiovascular treatment and modeling the development of non-Hodgkin’s lymphoma to develop a progressive treatment regimen.
In addition to improving development and unlocking more insights, real-world evidence has been a major cost-cutting tool for pharmaceutical companies. In a 2020 report, McKinsey & Company said the typical top-20 pharma business that implements real-world evidence across its value chains for products in the market and in development could realize more than $300 million in value over 3-5 years.
The research company said pharma businesses could save $100 million in R&D spending by using analytics to capture more insights from real-world data rather than from clinical trials — as well as using it to optimize clinical trial design and implement synthetic trial arms. Additional value could be realized by identifying more potential therapeutics, speeding up time to market, improving payer negotiations, and producing stronger proof of differentiation for products in the market. Additionally, real-world evidence is enabling a more proactive response to adverse treatment events, saving lives and potentially avoiding costly litigation.
McKinsey went on to say that emerging artificial intelligence technologies promise to realize even more value and open up more business possibilities. Technologies like generative adversarial networks (GANs) are poised to cultivate even more insights from the ongoing expansion of medical data found in electronic health records, insurance claims, wearables, consumer records, social media, and patient-reported outcomes.
Fueling Pharma Innovation with TripleBlind
Extracting more value from all of this data is well within our grasp, but access limitations are proving to be a major obstacle. The TripleBlind Solution helps companies unlock siloed sensitive and non-sensitive data to provide added value across the entire pharma value chain.
Because TripleBlind allows organizations to access data directly from data providers and maintain pharma data integrity, it enables all kinds of possible actions, such as the identification of top-performing analytics entities and subject matter experts. Our technology also enables more secure collaboration between pharma companies and data providers.
By enabling secure access to data, continuous real-time secure operationalization of data, and improved consistency with regulatory needs during post-market monitoring, the TripleBlind Solution facilitates the transition of real-world data to real-world evidence, unlocking untold value in the process.
If you would like to learn more about how TripleBlind can fuel pharma innovation, please contact us today.