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Where is the value in big data?

Big data has been hailed, in all manner of industries and arenas, as the ultimate tool with which to uncover the real effects of change and behaviors and to inform decision making. But does it live up to the hype?

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Healthcare costs are unsustainable. In the US alone, per capita spending on healthcare more than tripled between 1996 and 2014, and there are no signs of this decreasing in the future.1 Changes are needed to bring down spending and stop healthcare systems collapsing under the burden placed on them. But in healthcare, it’s not enough to just talk about the cost of new treatments or interventions. We need to take a wider view; to establish what good value looks like in terms of benefits to patients as well as reducing the burden on healthcare systems.

Big data has been hailed, in all manner of industries and arenas, as the ultimate tool with which to uncover the real effects of change and behaviors and to inform decision making. But does it live up to the hype?

In healthcare, new technologies are providing us with the opportunity to capture more and more types of data on people’s behavior and the impact of medical interventions on outcomes. Now we want to use this data to gain new insights into when (and how) people take their medication and how well they are managing their disease. We want to use these insights to inform dialogue between healthcare professionals and their patients and to help patients become more involved in their own disease management.

People already recognize the potential for improved data collection and analytics to improve their own health. They use personal health monitors to track their exercise, their eating habits and their sleep patterns. In clinics, spending on remote patient monitors specific to different conditions more than doubled between 2007 and 2011.2 So why not use these new and upcoming technologies to track, not just health status and medicine adherence, but also behavior and symptoms; to identify trends and to link these to individual risks.

We know that data from remote monitoring technologies, when combined with predictive models, can be used to help identify problems more quickly and open the way to predictive, preventative medicine. In respiratory care for example, this potential for predictive medicine and risk-based interventions has been recognised with rescue inhaler use as a strong predictor for risk of exacerbations in asthma.3

Data analytics can also be used more simply to improve efficiencies and reduce costs by simply preventing unnecessary repetition. For example at Texas Children’s Hospital, where people with asthma were found to be receiving a large number of unnecessary chest X-rays. By simply consolidating data sets, they managed to achieve a 46% reduction in unnecessary interventions, with an associated reduction in length of stay for patients, and healthcare costs.4

All of this means collecting the right data and knowing how to use it to both demonstrate and increase value. Tapping into the potential of big data could transform the way healthcare is delivered; providing not only economic benefits, but having the potential to improve clinical outcomes, but it needs to be approached the right way. To find out more about how we think data can inform clinical decision-making and reduce costs in healthcare, explore our second whitepaper, ‘Redefining value in healthcare: how data can help improve outcomes and increase value’—available for download here.


  1. World Health Organization Global Health Expenditure database 2014. Available at: Accessed: March 2018.
  2. Sales of Remote Patient Monitors Up Almost 20%: Report. Available at: Accessed: March 2018.
  3. Merchant RK et al. J Allergy Clin Immunol Pract. 2016; 4(3): 455–463.
  4. The Top Success Factors for Making the Switch to Outcomes-Based Healthcare. Available at: Accessed: March 2018.

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