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Big Data and the Convergence of R&D and the Commercial Organization
In previous columns, I’ve discussed a number of aspects of market access, like market segmentation, use of real world evidence, and changing payer perspectives. This month, I focus on another topic that’s become popular across the healthcare industry recently: big data.
Big data is quickly becoming the latest buzzword. But as organizations begin gathering more data on their own or forming partnerships to do so, it’s increasingly clear that having the data isn’t enough. Pharmaceutical companies will have to figure out, and quickly, how best to use this data. Big data can support companies’ strategies, especially when this data is incorporated across the product lifecycle.
Having Data May Not Be Enough
As payers around the world try to figure out how to get spiraling healthcare costs under control, big data has been hailed as a silver bullet. The underlying premise is that, with enough data and enough analysis, we can figure out how to best treat patients for the least amount of money. It’s becoming clear that if payers, providers and regulatory agencies are beginning to look into this data, medical product makers will need to have data of their own in order to avoid being left behind.
As various stakeholders have begun aggregating data, it’s clear that having the data in and of itself is not enough. Data aggregation matters much less than how the data is used. There are numerous opportunities for pharmaceutical companies to use big data. For example, they can use this data to assess products currently on the market to investigate anecdotal claims about alternative uses, verify possible side effects that didn’t appear in a controlled setting, or to confirm the existence of unmet market needs.
“Companies need to understand how other stakeholders in the industry are using big data. From patients to payers and providers”
So how can pharmaceutical companies ensure that they use big data to support these advantages? For some time, we’ve said that in order to create sustainable differentiation, manufacturers will have to use marketing insights to guide R&D decisions, reflecting a more holistic view of product lifecycle value and the continuum of care. For big data to support this view, pharmaceutical companies will need to use this information across the product lifecycle from early development to post-market. Then, companies can supplement anecdotal marketing insights with evidence, which can help them refine their development strategies further.
Using Big Data
Building a big data strategy that’s integrated across multiple organizational groups will be a start, but there are other considerations for pharmaceutical companies’ big data strategies as well. Companies need to understand how other stakeholders in the industry are using big data. From patients to payers and providers, there are all kinds of tools and partnerships that can have implications for pharmaceutical companies:
- Patients are increasingly using tools like ZocDoc to compare cost and quality of care, and are using social media to share their experiences.
- Providers like Aurora Healthcare are investing in data warehouses, too. Aurora, a provider organization, partnered with Oracle to examine their data to conduct research including CER, safety monitoring and long-term studies.
- And payers have been acquiring health information companies to analyze data of their own, as UnitedHealthcare did with Axolotl.
Each of these stakeholder groups has expressed interest -- and invested resources -- in additional data analyses. It is likely that these groups will use data to influence treatment decisions, so pharmaceutical companies need to be prepared to anticipate and address possible objections. As part of this “due diligence,” pharmaceutical companies need to understand these partnerships and how these organizations are looking at data and drawing conclusions from it.
“As payers try to get costs under control, companies that can demonstrate with evidence that they have the right product for the right patient at the right time will be better able to secure premium reimbursement and market access”
They will also need to explore partnerships of their own, and some have begun to do that. Pfizer has partnered with Medco to conduct research on personalized medicine. Other life science companies are launching partnerships with genomics firms or investing in data resources of their own. When used in the context of a market model for pharma, where the goal is identifying R&D opportunities based on unmet market needs, these types of collaborations can be critical to the development of products that are differentiated based on economic and clinical value. This means thinking differently about relationships with other organizations that may be able to supplement your own data, including managed healthcare companies.
Finally, part of a company’s strategy for data can be the identification of specific sub-segments of the patient population where products will work best. Or conversely, looking at those subsets where the product doesn’t work or at least not as well as a cheaper alternative. As payers try to get costs under control, companies that can demonstrate with evidence that they have the right product for the right patient at the right time will be better able to secure premium reimbursement and market access.
Pharmaceutical companies that have adapted their processes and structure to integrate data usage across the product lifecycle will be uniquely suited to make the most of it. Additionally, these companies will be positioned to make optimal use of partnerships around data analysis. Big data will be a feature of the healthcare landscape going forward, and companies need to ensure their products can stand up to increasing scrutiny.
But the power of any data, large or small, is in how it’s used. And that ties back to the ability of commercial experts and R&D to generate, on a collaborative basis, the critical hypotheses that need to be tested systematically. Without this analytical rigor, erroneous conclusions can be drawn that may have detrimental effects on a product or an entire portfolio!
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