Drug discovery and development is a complex and time intensive process that requires a significant amount of resources and capital investments (~USD 2.6 billion). In fact, on an average, the journey from establishment of initial proof-of-concept to product launch, takes around 10-12 years. Moreover, regulatory approval is dependent on the results of randomized clinical trials (RCTs), which are estimated to account for a staggering 40% of the pharmaceutical industry's budget in the US. It is also well known that clinical research is fraught with various other challenges, including inefficiencies in medical data management and processing, unforeseen delays, risk of failure / study termination and several patient recruitment and retention-related concerns.
Further, since such trials are conducted under controlled conditions and involve a fairly homogenous patient population, there are chances that, post commercial launch, approved products fail to perform as expected. In this context, the application of insights from real world data, accrued from past trials, has been demonstrated to have the potential to save up to USD 1 billion per year. In fact, real world evidence can actually complement results from controlled RCTs, thereby, validating the therapeutic potential of a new chemical / biological entity.
In December 2016, after the 21st Century Cures Act was passed, the United States Food and Drug Administration (USFDA) began considering the application of real world evidence in healthcare decision-making. Ever since, pharmaceutical companies and health economists have developed advanced tools and analytical algorithms to mine pharmaceutical big data, in order to better understand the clinical value of product candidates targeting some of the rarest medical conditions. Presently, big pharmaceutical companies are estimated to spend nearly USD 20 million on an annual basis, on generating real world evidence to support their respective clinical development programs. Over time, insights from real world data have not only influenced product approval-related decisions, but also helped convince insurance provider / payers into offering reimbursement for new drugs / therapies. The growing adoption of artificial intelligence and machine learning in big data analysis, is anticipated to better inform future drug discovery initiatives, thereby, reducing the risk of product failure. The adoption of real world evidence in healthcare decision making is projected to grow substantially as the healthcare industry shifts towards the personalized medicine.
The report features an extensive study of the current market landscape and future potential of the industry players that are engaged in offering real world data / analytics / services to the pharmaceutical and life sciences industries. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain.
The USD 4.5 billion (by 2030) financial opportunity within the real world evidence market has been analyzed across the following segments:
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