18 hours ago · The ‘big data’ revolution in healthcare: Accelerating value and innovation (PDF–1.4 MB), January 2013; and Ajay Dhankhar et al., “ Escaping the sword of Damocles: Toward a new future for pharmaceutical R&D ” (PDF–1.8MB), McKinsey Perspectives on Drug and Device R&D 2012 . estimates that applying big-data strategies to better inform ... >> Go To The Portal
Although the health-care industry has lagged behind sectors like retail and banking in the use of big data—partly because of concerns about patient confidentiality—it could soon catch up. First movers in the data sphere are already achieving positive results, which is prompting other stakeholders to take action, lest they be left behind.
Meanwhile, the US federal government and other public stakeholders have been opening their vast stores of health-care knowledge, including data from clinical trials and information on patients covered under public insurance programs.
Since 2010, more than 200 new businesses have developed innovative health-care applications. About 40 percent of these were aimed at direct health interventions or predictive capabilities. That’s a powerful new frontier for health-data applications, which historically focused more on data management and retrospective data analysis (exhibit).
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Our research suggests that by implementing eight technology-enabled measures, pharmaceutical companies can expand the data they collect and improve their approach to managing and analyzing these data.
For a big-data transformation in pharmaceutical R&D to succeed, executives must overcome several challenges.
Jamie Cattell is a principal in McKinsey’s London office, Sastry Chilukuri is an associate principal in the New Jersey office, and Michael Levy is an associate principal in the Washington, DC, office.
To understand the potential of “big data,” look no further than the US healthcare sector. Information silos between payers and providers are crumbling, enabling the powerful integration of digitized public historical and real-time data.
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The role of big data in medicine is one where we can build better health profiles and better predictive models around individual patients so that we can better diagnose and treat disease.
Wearable devices and engagement through mobile health apps represent the future—not just of the research of diseases, but of medicine. I can be confident in saying that, because today in medicine, a normal individual who is generally healthy spends maybe ten minutes in front of a physician every year.
What I see for the future for patients is engaging them as a partner in this new mode of understanding their health and wellness better and understanding how to make better decisions around those elements.
One of the most fun aspects of creating the Icahn Institute—and growing it into the state it’s in today and where it’s heading—is creating the right kind of ecosystem that can be comprised of highly diverse individuals from the standpoint of different areas of expertise.
Pharmaceutical R&D must continue to use cutting-edge tools. These include sophisticated modeling techniques such as systems biology and high-throughput data-production technologies—that is, technologies that produce a lot of data quickly, for example, next-generation sequencing, which, within 18 to 24 months, will make it possible to sequence an entire human genome at a cost of roughly $100.
Organizational silos result in data silos. Functions typically have responsibility for their systems and the data they contain. Adopting a data-centric view, with a clear owner for each data type across functional silos and through the data life cycle, will greatly facilitate the ability to use and share data. The expertise gained by the data owner will be invaluable when developing ways to use existing information or to integrate internal and external data. Furthermore, having a single owner will enhance accountability for data quality. These organizational changes will be possible only if a company’s leadership understands the potential long-term value that can be unlocked through better use of internal and external data.
Real-world outcomes are becoming more important to pharmaceutical companies as payors increasingly impose value-based pricing. These companies should respond to this cost-benefit pressure by pursuing drugs for which they can show differentiation through real-world outcomes, such as therapies targeted at specific patient populations. In addition, the FDA and other government organizations have created incentives for research on health economics and outcomes.
To ensure the appropriate allocation of scarce R&D funds, it is critical to enable expedited decision making for portfolio and pipeline progression. Pharmaceutical companies often find it challenging to make appropriate decisions about which assets to pursue or, sometimes more important, which assets to kill. The personnel or financial investments they have already made may influence decisions at the expense of merit, and they often lack appropriate decision-support tools to facilitate making tough calls.
Pharmaceutical companies can use safety as a competitive advantage in regulatory submissions and after regulatory approval, once the drug is on the market. Safety monitoring is moving beyond traditional approaches to sophisticated methods that identify possible safety signals arising from rare adverse events. Furthermore, signals could be detected from a range of sources, for example, patient inquiries on Web sites and search engines. Online physician communities, electronic medical records, and consumer-generated media are also potential sources of early signals regarding safety issues and can provide data on the reach and reputation of different medicines. Bayesian analytical methods, which can identify adverse events from incoming data, could highlight rare or ambiguous safety signals with greater accuracy and speed.
Pharmaceutical R&D has been a secretive activity conducted within the confines of the R&D department, with little internal and external collaboration. By breaking the silos that separate internal functions and enhancing collaboration with external partners, pharmaceutical companies can extend their knowledge and data networks.