Optimising cohort data in Europe

On the surface, trust can be quantified by consent arrangements and confidentiality guarantees. However, the origins of a resource such as trust are marked by unique historical conditions (e.g. people are more willing to trust researchers in critical situations such as during a pandemic), causal ambiguity (i.e. there is still no clarity regarding the motivations of governments, pharmaceutical firms and the health sector) and social complexity (i.e. there is no guarantee that research institutions will not turn rogue later on). Hence, trust is not a substitutable resource: once it is lost, there is no more possibility for cohort research. Such characteristics mean that trust is a highly specialised but volatile (i.e. once consent is lost, it becomes very difficult to regain it). A way to mitigate this volatility is to add quantifiable, measurable dimensions to trust (i.e. converting trust into a tangible and quantifiable resource). This can be done through knowledge integrationmechanisms such as rules and directives. According to the knowledge-based theory, rules and directives generally regulate the applications of knowledge and the collaboration between those who hold specialised knowledge. In the context of cohort research, this means that we should install checks and barriers to data misuse. This can be done through a governance system operating in the long term, with regular control mechanisms over time. Examples of such control mechanisms can be institutionalised arrangements for whistle-blower protection as well as the legal enforcement of individual responsibility and liability for data misuse. Another solution is to structure the intangibility and volatility of trust through temporally based routines as well as sequencing processes. That is, in the control-trust interaction, the issue of control gains more importance over time. For instance, data subjects are likely to trust their institutions (and their respect for human dignity and democratic values) and thus, give their consent for the use of their personal data. However, this does not mean that they will trust the same institutions a decade later. Thus, in order to preserve trust over time, data subjects’ control over their data is essential. The main question, therefore, is how we can ensure that data subjects have the right tools to fully exert their right to data control. This question is not easily solved given the increasing use of emerging digital data collection technologies (i.e. eHealth) in cohort research. Modern digital technologies problematise the issue of control because their structures and modes of operation distribute control away from users and dilute personal agency and awareness. This is partly motivated by participants’ lack of awareness of their own autonomy: they tend to readily agree to terms and conditions of the platforms they use without understanding the implications for doing so. Autonomy and control is thus not something that can be taken for granted because it can be easily exchanged for perceived benefits (such as the services of an app). In fact, few users have really full control of their privacy rights in digital platform settings. This gradual loss of control is amplified by the lack of integration of eHealth data into existing national and local medical records.