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Real-world evidence (RWE) has transformed Life Sciences workflows, affecting nearly every aspect of drug development, commercial strategy, and outcomes research. Thanks to RWE and powerful software that is capable of parsing that data for nascent trends, clinical trials are fielded faster with more diverse study populations, new therapies can be fast-tracked to the patients who need them most, and strategies are continually being adjusted and refined based on near real-time inputs. But those benefits are not without cost and complications.
The rapid-fire growth of RWE and the corresponding emergence of data marketplaces that specialize in selling it have created a new set of challenges for Life Sciences teams who’ve suddenly found themselves spending more time and resources than ever sorting out complex data integrations and organizing disparate, duplicative, and incomplete datasets.
In fact, according to our new study, Life Sciences teams are spending an average of seven months and working with four different data vendors and four consultants just to obtain and prepare the data they need to conduct RWE-driven analyses. The study is based on a detailed survey of 300 senior leaders, managers, and data scientists/analysts working in Clinical Development, Commercial, or HEOR functions at small ($50M+), medium ($500M+), and large ($1B+) Life Sciences companies. In addition to benchmarking the current state of RWE in various aspects of the Life Sciences workflow, the research also highlights some of the most pressing pain points that are keeping RWE-driven analytics from reaching their fullest potential.
Citing an array of challenges ranging from data reconciliation issues when working with multiple sources to inconsistent management and cloud storage approaches, the survey respondents shine a spotlight on many of the growing pains that have emerged over the last several years of RWE growth.
With so much attention being given to data solutions enhanced by artificial intelligence (AI), it is interesting to note that AI is not yet delivering on the promise of democratizing data insights. While 80 to 85 percent of medium and large organizations surveyed use AI-enhanced data solutions, smaller organizations were two to three times more likely to state their data solutions were not leveraging AI.
The study also highlights the rapidly emerging opportunities around enterprise-wide approaches to data and analytics. As we continue to train our sights on delivering fully integrated, full-stack approaches to RWE, legacy silos between disparate datasets are disappearing and more choreographed, harmonized RWE strategies are becoming a reality. The industry still has some work to do to start unlocking the full potential of RWE, but this research offers a roadmap for overcoming those challenges.
To learn more about the insights from this study, explore the ebook.
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