Editorial: The personalisation of insurance: Data, behaviour and innovation

Abstract

The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

The Sociotechnical Ethics of Digital Health: A Critique and Extension of Approaches From Bioethics

“…The ways in which health care systems operate is very much in the public interest, broadening the range of ethical issues deemed relevant to the ethical analysis of digital health. One important point worth mentioning here is the impact of organizations such as insurance companies that use digital health technologies to collect information about individual behaviors and shape their product offerings accordingly ( 63 ). Such practices are made newly effective by advances in digital health technologies, and the role they ought to play in the insurance industry going forward is an organizational policy issue requiring close ethical attention.…”

Big Data = Big Trouble for Universal Healthcare? The Effects of Individualized Health Insurance on Solidarity

“…Exacerbated by ever more data, new and better techniques of data analysis such as predictive analytics coupled with selflearning algorithms show great promise in forecasting individual health trajectories (Alyass et al, 2015;Blasimme et al, 2019;Vayena et al, 2018). Consequently, many authors highlight the disruptive potential of Big Data applications in HI pricing (Albrecher et al, 2019;Cevolini & Esposito, 2020;McFall et al, 2020). More specifically, the increasing collection and analysis of Big Data regarding mental and physical health information coupled with new data analysis tools help granularly assess the individual health risk dispositions of patients (Blasimme et al, 2019).…”

“…Personalized medicine tailors prevention and treatment to the individual based on digital health data (Alyass et al, 2015;Cirillo & Valencia, 2019;Hamburg & Collins, 2010;van Dijck & Poell, 2016). The health insurance (HI) industry also increasingly explores options to reap benefits from the continuous collection of large digital data sets (Cevolini & Esposito, 2020;Janssen & Corlosquet-Habart, 2018;McFall et al, 2020). That is, HI contributions can be more finely tuned to the risks of individual policyholders (Ostrowska, 2020;Pnevmatikakis et al, 2021).…”

The FDA’s standard-making process for medical digital health technologies: co-producing technological and organizational innovation

“…As such, DHT are the product of a process of ‘medicalization’ and ‘lifestylization’ at the same time: they enable the entry of lifestyle data (e.g. sleep tracking information, step counting, and dietary habits) into the medical sphere, while medicalizing people’s lifestyle choices at home (Lucivero and Prainsack 2015 ; Joyce 2019 ; McFall, Meyers and Van Hoyweghen 2020 ).…”