Real world data

Real world dataIn your average week, consider how many apps you use on your smartphone – aside from the expected range of social media apps, are you using any apps that could improve your healthcare? Even without added smart devices from companies such as Fitbit and Scanadu, the phone in your jeans pocket is capable of keeping track of your health, and improving it.

The proliferation of health apps – at the end of 2014 there were 100,000 apps listed across the health sections of the major app stores[1] – highlights the growing importance of real world data. Looking at just a few examples of the types of apps on offer – patients can now track data like calorie intake, heart rate, active minutes, ovulation cycles, sleep quality and much more directly from their phones. As the apps and connected sensors continue to develop, much of this data collection is automatic, not even requiring manual user input. Such real world data, Aurora’s fourth Access All Areas dependency, have huge potential for patients, the NHS and pharmaceutical companies.

For the patients, apps and the data they produce has the potential to save lives by highlighting a change in health – such as heart problems. For the NHS, real world data can help to personalise a patient’s care as well as saving resources. For pharmaceutical companies, real world data can be used to monitor the impact of medicines on patients in context and over time. This kind of evidence can help to offset the reliance on ‘cost-effectiveness’ data which can sometimes act as a barrier to medicines access.

It seems that the use of real world data should not be met with resistance – however there are some fears that the data could be used in service of negative agendas – counting for the sake of counting and using data as a stick to beat others with. Despite this, real world data has great potential for showing the value of a medicine which outweighs these potential fears. There is a further fear that data will reveal uncomfortable truths about either medicines or the system itself. Yet uncomfortable truths should not be seen as a risk or fear – but as an opportunity for improvement.

An example of this can be seen in the case of Larry Smarr. Larry, a leader in scientific computing, moved to California in 2000. Here he became interested in exercise, health and fitness. He began to keep daily track of his body – initially starting with weight. As he became more interested in nutrition and health and more intent on losing weight, he began to measure more data. This eventually led to him measuring a key blood-marker, C-reactive protein. The collection of this data led to him discovering that he had Crohn’s disease, despite being initially told by his colonoscopist that he did not have Inflammatory Bowel Disease including the subclass of Crohn’s. Although Smarr’s collection of real world data are atypical at this time, in the future it will be made much simpler and has the potential to increase early diagnosis and encourage preventative rather than reactive healthcare.

The growth of real world data comes alongside a shift in the evidence required for releasing funding for medicine uptake. Clinical and cost-effectiveness data are no longer enough, and real world data can be used to bridge the gap. It is still an emerging field, but in the meantime there are plenty of recommendations to consider which will allow us to begin using real world data:

  • Ask yourself how much importance you place on real world data? To what degree are you gearing up for collecting it?
  • How do we get commissioners to consider real world data as a credible source of information? What information will they see as useful in their day-to-day roles, and in what formats?
  • Think about what interim actions you can take as we wait for real world data to become viable at scale
  • As you set up valuable support tools for patients, think about data collection. If your organisation’s policies prohibit collection of anonymised data, perhaps it is time to challenge this
  • Consider the role of real world data for the duration of the medicine lifecycle – from development to patent expiry
  • Consider what it might mean to be collecting and interpreting data throughout the patient treatment journey, rather than at discrete intervals. What constraints does this highlight?
  • What might it mean if data develops new meanings over time e.g. new products, new health behaviours?
  • How ready are you for data to reveal uncomfortable truths? How can these truths be presented honestly and constructively?

To find out more about this dependency and the other dependencies, read our Access All Areas paper, ‘Creating opportunities for improving patients’ access to medicines’, available for download here.

[1] mHealth App Development Economics 2014, mHealth Economics, p.11
http://mhealtheconomics.com/mhealth-developer-economics-report/

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