Healthcare costs are unsustainable. In the EU, public expenditure on healthcare is predicted to rise from 8% of GDP in 2000 to 14% in 2030, and continue to grow beyond that date.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 as the coming saviour in all manner of industries and arenas as the ultimate tool with which to uncover the real effects of change and behaviours 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 behaviour and the impact of medical interventions on outcomes. Now we want to use this data to gain new insights into how (and when) 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 to become more involved in their own disease management.

People already recognise 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. From 2013 to 2018 the European market revenue from wearable devices and services has grown from €13 billion to €23.1

billion, and is expected to increase by an additional €9 billion by 2020.2 So why not use these new and upcoming technologies to track, not just health status and medicine adherence, but also behaviour 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.