One of the difficulties faced by those trying to unlock the value in data collected by systems from individuals is that even when the data can be made anonymous, the owners of the data are reluctant to share it with third parties.
But in clinical trials, and in other medical research projects, data is routinely collected, shared and analysed. What does this tell us about the frameworks for trust that are needed to make the most of “big data”?
One thing it’s important to understand is why individuals – and organisations – choose to allow or disallow the use of their private data.
While trust in the organisation that has collected or is asking for your data is a major consideration (see for example research here), there are also the related issues of the purpose of the work, and what is given back in return.
Personal benefit and the “public good”
Sometimes the links to purpose and return are immediate and obvious – as when we as individuals agree to our location being shared in real time so we can use map applications on our smartphone. But at other times it’s much more subtle.
In medical trials, individuals both trust the medical establishment (our GP, or a university department, or “the health system” generally) we understand the specific purpose, and we get back something that might help us (but probably won’t) or might help others much later. (We may get paid too, but not very much.) There is altruism at work here – and it’s towards a general concept of “the public good” rather than to a clearly defined person or group.
When it comes to organisations that hold private data (for example local councils’ transport and traffic data, or retailers’ transaction data) the details of motivation and barriers to sharing are a little more complex but still boil down to trust, purpose and return. In some cases, particularly where data has been collected from individuals, the purpose of the work must be allowed legally – even if data is anonymous. The issues of trust become wrapped up in legal contracts and non-disclosure agreements. The return becomes defined in terms of financial benefit to the organisation as well as the benefit to any individuals whose data is used.
Innovation in data collection and sharing for medical research
Freed from the difficulty of establishing trust, the health sector has been developing innovative ways to collect specific data for research purposes. Noteworthy examples are data collection and sharing platforms that can be partitioned to provide databases related to specific diseases, with user-friendly portals and interfaces for patients and doctors to log symptoms, test results, and adherence to medication regimes.
These have been developed and used by companies such as www.umotif.com and www.patientsknowbest.com to support projects including research into Parkinson’s disease, diabetes, maternal and newborn care, and others. Such platforms integrate with fitness monitors, apps, hospital, local authority and pharmaceutical company databases and a whole host of other devices, sources and analytical systems, on a project-by-project basis.
Lessons to be learned
Thinking about the success of platforms like uMotif, my recommendations for those aiming to develop ways in which personal data can be collected, shared and analysed are: