This blog is part three in a series exploring how to leverage real world data to transform brand planning, launch and management created in collaboration with Fierce Pharma.
Commercial teams at life sciences companies need to reinvent their go-to-market strategies. In doing so, however, they are apt to face a variety of challenges such as:
- An incomplete view of the healthcare ecosystem.
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The inability to illustrate value.
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A limited understanding of the patient’s plight.
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Lack of access to comprehensive data.
To transform raw data points into useful insights for these purposes, life sciences teams need to go beyond the simple retrospective analysis of any single data point/type. Only by working with a partner that has the right combination of domain expertise, data coverage and appropriately leverages technology to achieve rapid insight generation at scale, life sciences marketing teams have the insights they need to drive the greatest return on their investments by:
Understanding the most appropriate applications of various data types
Go-to-market teams need a nuanced comprehension of the intricacies associated with each data type. These teams must leverage insights across claims, testing, nomic, clinical trials, operational, regulatory, manufacturing, consumer and many other data types. How to do this appropriately and in a compliant manner is pivotal for constructing a comprehensive treatment mapping that effectively addresses patients, payers, and provider needs.
Creating a holistic view via data tokenization
With data tokenization, go-to-market teams can construct a common foundation to integrate disparate data sets and disparate data types to generate a holistic view of the patient, providing the insights needed to address challenges and opportunities across the patient journey, without compromising patient confidentiality.
When it comes to real world data, no data set is perfect on its own. There is no singular data set that’s going to tell go-to-market teams everything about the patient and about their engagements with the healthcare system ... So tokenization is the first step in quickly bringing those siloed data sets together so that life sciences companies can have a more comprehensive view.
Leveraging various data vocabularies
Just like there is no perfect singular data file, there are a multitude of taxonomies and data vocabularies that are used throughout patient encounters and treatment. As such, life sciences teams can expedite insight generation by working with a partner that has the ability to manage this crosswalk and alignment. PurpleLab® has spent a lot of time transforming the vocabularies that are used within those different data sets to provide a ‘Rosetta Stone-like’ view, making it possible to interchangeably use various vocabularies to tell a cohesive story, so that our customers can focus on actioning insights, not managing data,” Brosso said.
Understanding data from external sources
“Historically, this would’ve been managed by large data governance teams. Now, because of the specificity required within precision medicine, the brand and commercial teams are more and more involved in those discussions,” Brosso said. As such, these teams require a partner that can help guide their data inventory and purchasing strategies to ensure a complete view of patient understanding.
Putting it all together
By leveraging partners that have experience with both the technology and processes needed to help ensure that insight generation is done in a way that does not compromise data quality, data can be collected for one reason – and then repurposed for another.
Medical claims are a perfect example of that value creation: originally generated to seek payment for a provided service by the provider ... it can be transformed and enhanced for use within clinical operations to measure patient burden of disease and HEOR teams to identify specific patient subpopulations that may be underserved within the healthcare landscape. This requires a tremendous amount of lift and effort on life sciences teams to transform that data to insights. So, to then place the burden upon brand teams to understand all of the nuances in that market is probably unrealistic.
