Riding the data stream
We are in the midst of a healthcare data explosion. Grappling with the information overload will be a struggle for pharma but this will be the future frontier – welcome to the datasphere
Senior Reporter: Katrina Megget. Edited by: Claire Bowie/Jenny Hone
Big brother is watching you. He knows the public transport you took to work, what you bought for lunch, from where and how much it cost, if you went to the gym, and possibly what colour socks you’re wearing. Meanwhile, thirty billion pieces of information are shared on Facebook every month, global smartphone penetration is growing at more than 20% a year, and in 2010 alone consumers stored more than six exabytes – in excess of six billion gigabytes – of new data on PCs and notebooks. And that’s just day-to-day life. In the healthcare arena, data generation has gone crazy, driven by the increased use of electronic medical records, technological advances in genomic sequencing and monitoring devices, and an influx of patients and doctors talking online.
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With so much data floating in cyberspace, it’s no wonder consumers, companies and economic sectors are exploiting its potential. Indeed the McKinsey Global Institute believes the world is “on the cusp of a tremendous wave of innovation, productivity and growth as well as new modes of competition and value capture” – all courtesy of a data revolution.
In essence, it’s an exciting time for pharma and healthcare because accessing the data stream opens up a whole new world of possibilities. In R&D, for instance, genomic and biological data can aid disease understanding and inform the design of products, trials and treatment decisions, while online social networks and health records offer up a huge repository of real-world patient data that can be used to improve and customise drug development and services, identify undiagnosed patients and serious adverse events, predict hospital re-admissions and other medical forecasting, and zone in on health outcomes and comparative effectiveness.
Meanwhile, real-time data can be used for tracking drug launches, referral patterns, blockages in the supply chain or patient pathway, drug switches and off-label use, as well as disease trends and locations. And for communications purposes, pharma and healthcare systems can monitor public sentiment, receive customer/patient feedback, ratings and reviews and then deliver targeted, relevant content and improved services.
Data also have the potential to transform the healthcare environment by improving communication, workflow and the patient journey – just by virtue of sharing information between GPs, hospitals and labs and enabling patients to be more active in their own care. But it is the cost, quality and efficiency aspect that excites many.
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The McKinsey Global Institute anticipates that data will not only form the cornerstone of future value-based pricing but will be used for gaining and defending market access, enhancing productivity and competitiveness, and generating substantial economic surplus. For instance, it estimates that if US healthcare could use so-called ‘big data’ (datasets whose size is beyond the ability of typical database software tools to manage) to drive efficiency and quality, then the potential value realised could be worth more than $300 billion every year – two-thirds of which would be in the form of reducing national healthcare expenditures. The report also goes on to say that medical clinical information providers – those that aggregate and analyse data to improve healthcare efficiency – could compete in a market worth more than $10 billion by 2020.
It is a rosy picture, yet the reality throws up some curveballs. On a general level the primary complaint in moving forward is siloed, unconnected data – currently much is discarded because it is physically impossible to store it all and there are neither rewards nor incentives for data sharing. Meanwhile, the security, privacy and big brother aspects are also issues waiting to be addressed.
For pharma and healthcare specifically these problems can really put the brakes on the ability to capitalise on data potential. For example, explains Jamie Cattell, partner in McKinsey’s pharmaceutical practice, it is difficult to build an end-to-end picture across specific disease pathways because data sits in silos both geographically and across different care settings, while prescription information is not well linked to primary or secondary care data and connections with social care varies widely by system. This “noise” can make data difficult to aggregate and interpret but, above all, often this holistic view is prevented because pharma lacks access to the healthcare system’s rich data sources in the first place. [However, in the autumn budget, the UK government announced various means to improve data transparency and linking in the NHS, which should go some way to alleviating the data constraints.]
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Cracking R&D productivity
But it’s not just an issue with the healthcare system, Cattell stresses. Pharma is generally “reactive” and creates its own internal barriers to the data being used; often such data are only employed to defend product value propositions, he says. Then departmental silos mean some parts of the company will know the data really well while others may not have good access or understanding – for instance, he says, there are typically pockets of excellence in R&D but generally these are not linked with commercial objectives or activities. “Overcoming these silos could lead to the ‘holy grail’ of linking clinical (outcomes, interventions) with economics and the scientific/patient level. Information could therefore become an important tool in the armoury to crack R&D productivity in the long run.”
Steve Mott, managing director of DrugLogic, believes data silos can be useful in specific instances but the problem arises, he agrees, when they are either inaccessible or cannot communicate with others. “The key is to unite those datasets so they can be managed individually but are also accessible via linkages that use a common identifier such as the NHS patient number… If the NHS, for example, can sort out and liberate the data – anonymously of course – then we can set about developing the computational and analytical capability to translate the information.”
Indeed, this represents another problem that Mott points to – poor computational capacity and lack of software capable of handling such a huge data load. Heather Fraser, global life sciences leader at IBM Institute for Business Value, agrees this is a major challenge for the industry in moving forward. According to a recent IBM study, 100% of life sciences chief marketing officers “confessed” they were “underprepared” to manage the impact of the data explosion over the next three to five years. “The growing velocity of the volume, variety, and granularity of information is driving new, unprecedented complexity,” says Fraser. “Additionally, much of this information is unstructured, or not in a format that can be easily tapped for innovation or used for quick decision making to keep these companies nimble.”
However, Fraser warns, it’s not just about pharma being able to access and compute the data sufficiently: it’s about having the right data to drive insights. “As the industry changes its current business models, analytics will be key to understanding and reacting to change both within their own companies and across the whole healthcare ecosystem… Over the next two years, pharmaceutical and life sciences organisations are challenged with how to innovate for differentiation, to reduce costs and manage regulatory compliance. Data are key but it requires the ability to integrate and harness the information no matter where it resides.”
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One way to gain insight is by sharing data but, as Craig Robertson, global director, Accenture Life Sciences Sales and Marketing, says, a win-lose pattern of healthcare management still exists where the fear is losing an advantage over competitors. Pharma needs to realise that sharing information will result in greater insight which, in turn, leads to increased innovation. The issue, he says, is that many of the current data sources are not timely, don’t provide the metrics needed for outcomes driven analytics and won’t be shared.
Changing this mindset, mining the data to pull out the information, and subsequent responsiveness, will be the biggest challenges for industry, both Cattell and Mott believe. Some companies are already partnering with data owners and analytics providers to harness new forms of information – for example, AstraZeneca and Healthcore (Wellpoint), Sanofi and Medco, and Pfizer and Humana – while others have a long way to go. Mott says pharma has to “reduce its dependence on analysis of historical data and under-reported voluntary systems and move into real-time, real-world data” in order to provide the value payers want regarding outcomes, drug safety, effectiveness, compliance, cost and the patient experience. Indeed, Cattell says, “anecdotally, some payers are already approaching pharma companies and saying ‘I’ll share my data with you – please come up with a way of optimising the health economy for disease X’”.
If this were not enough, pharma also has to keep up with the informed, empowered patient, with both individuals and doctors creating their own competing data sources and beginning to overcome some of the fears of putting personal health information online. Cattell believes pharma is entering a world where its customers will know more about its products than it does itself. And understandably this causes trepidation; pharma is effectively losing control of its data while others outside the industry are taking a disproportionately greater stake in it. Yet in saying that, Cattell believes “it feels like this is a big opportunity for pharma to show a different and more collaborative face to health systems”.
There will still be many hurdles to overcome as the industry moves to capture the value in data – to shape how the data are used, define what standards should exist to govern their use and ensure scientific rigour in analysis, as well as find new ways to collaborate. “There will be win-win opportunities but also win-lose situations,” Cattell says. “It’s too early to tell what the balance between the two will be, but it will be an interesting ride for pharma.” The use of data and the promise it holds has the potential to disrupt the healthcare industry in ways already seen in other sectors. It’s just a matter of time now before it fully immerses itself in the
data stream. PT
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