BLOG – Collaboration and the Safer Use of Health Data

The use of health data has had an interesting evolution. When I look back to the mid-1980s when I was in general practice in London, and first became aware of the important role computers can play in clinical care, there was initially a lot of resistance, negativity, and fierce debate about the ethics of sharing even anonymised patient data with pharma without their consent. There was generally a high level of discomfort about how the pharma industry would use this data, and initial approaches foundered on concerns around privacy.

Suffice it to say, the opportunity to use real world data has unfolded very slowly, but since the mid-‘80s and up till today’s GDPR , a succession of data protection legislation and directives has progressively strengthened the expectations a person should have about the safety, transparency and use of their health data. There have since been many technical innovations that allow for more robust anonymisation, and risk assessments of exposure of individuals – and more sophisticated research techniques using federated networks such as EHDEN.

In spite of these positive developments, there is still an imbalance in the public’s perception of the benefits of using real world data versus perceived risks. By and large, the public tends to only hear the cons and not the pros, and subsequently public debate is still too fear-focused. To improve this perception, we need to be more open and educate the public so they are aware of the true benefits of data-sharing and how not sharing data actually harms patients. We need to engage better with the public using clear and simple examples so that they see how their data is being safeguarded while being used for research, and are happy with it. Using the example of the COVID-19 pandemic can serve to illustrate to the public how real world data-sharing on a large scale led to the much faster development of vaccines that now have the upper hand in combatting this disease.

Fast-forwarding in my career to more recent times, I was involved in a number of European projects that looked at how we can better join up clinical care and help health organisations to better learn from their health data. There was a consensus among many stakeholders that there was a need to create a neutral and transparent organisation at the European level that focuses on health data that can serve as a catalytic force for better data value to all – and to identify and break down the barriers that are holding us back. This led to the creation of The European Institute for Innovation through Health Data, which I have presided over since its founding in 2016. Our mission is to promote, develop and share good practices, tools and quality assessments to maximise community value from health data for innovations in health, care and research. Our scope is broad and focuses on activities such as:

• Raising societal awareness about the benefits of using high quality health data
• Bringing key stakeholders together in a neutral forum
• Creating innovative solutions and education to support the re-use and sharing of health data
• Assessing and certifying the use of health data
• Catalysing change and improvement by engaging stakeholders in co-creating and then adopting health data usage solutions that influence healthcare decision-making

In summary, it’s clear that significant strides have been made in using the transformational potential of real world data, on a large scale, to generate real world evidence faster than ever before – and that this is translating into improved patient treatment and outcomes. But there will always be room for improvement, especially in terms of broadening the public’s understanding of these benefits. In a recent Voice of EHDEN podcast, I was asked about my vision for the future. I see three areas:

1. That we care more about health data and better leverage its enormous potential so that we invest in systems that collect and capture data in ways that make it easier for it to be computable, e.g., in clinical and patient systems, so that good quality, standardised data is fit for analysis purposes.
2. That we resolve, at least to a sensible level, issues and dilemmas around privacy versus the utility of data, such that we can perform a large number of learning analytics without complex, slow and time-consuming reviews. In other words, we establish computable rules that enable the learning from the data to happen in near- to real-time. This will significantly enhance the speed and ways in which we can tackle disease.
3. That we have human and organisational processes that take innovative learning and translate it into solutions that the public can actually get, as we saw so clearly in the speed at which COVID-19 vaccines were approved. This came as a result of strong collaboration – people, processes and rules. We need to have a method for making sure that what we learn from the data leads to genuine benefit – that’s my dream!