Guiding Pay-As-You-Live Health Insurance Models Toward Responsible Innovation in Health.

Hassane Alami,Pascale Lehoux,Lysanne Rivard,Robson Rocha De Oliveira,Mohamed Ali Ag Ahmed,Mathilde Savoldelli,Stéphanie Bernadette Mafalda Cadeddu,Jean-Paul Fortin

doi:10.2196/19586

Hassane Alami, Pascale Lehoux + Show 6 more

Open Access

https://doi.org/10.2196/19586

Copy DOI

Abstract

While the transition toward digitalized health care and service delivery challenges many publicly and privately funded health systems, patients are already producing a phenomenal amount of data on their health and lifestyle through their personal use of mobile technologies. To extract value from such user-generated data, a new insurance model is emerging called Pay-As-You-Live (PAYL). This model differs from other insurance models by offering to support clients in the management of their health in a more interactive yet directive manner. Despite significant promises for clients, there are critical issues that remain unaddressed, especially as PAYL models can significantly disrupt current collective insurance models and question the social contract in so-called universal and public health systems. In this paper, we discuss the following issues of concern: the quantification of health-related behavior, the burden of proof of compliance, client data privacy, and the potential threat to health insurance models based on risk mutualization. We explore how more responsible health insurance models in the digital health era could be developed, particularly by drawing from the Responsible Innovation in Health framework.

Highlights

Driven by the success of the quantified-self movement [5], PAYL is a variant of the Pay-As-You-Drive care insurance model, which relies upon a GPS device to track client driving behavior to inform insurer decisions regarding increases or decreases in insurance premiums [6]
With real-time feedback, many insurers offer clients mobile applications and sensors to motivate them to adopt healthy behaviors and good lifestyle habits [5,7]. When using these applications and sensors intended to collect data, clients must share a multitude of health data with the insurer to track progress and judge compliance with the insurer’s recommendations
We discuss the following issues of concern: the quantification of health-related behavior, the burden of proof of compliance, client data privacy, and the potential threat to health insurance models based on risk mutualization

Summary

Introduction

As health systems struggle to successfully implement a digital transition in care and service delivery [1], the ubiquity of mobile technologies combined with the emergence of the “quantified-self” movement has already generated a phenomenal amount of data on the health and lifestyle of individuals [2,3]. Individuals with low levels of digital and health literacy may have difficulty managing and interpreting large amounts of data and health information [9] Such a situation could lead to other problems, including stress, isolation, low self-esteem, deterioration of quality of life, and cognitive overload (“fatigue alert”) [18,19]. The RIH framework invites those who develop insurance models to respond to the most pressing population needs while reducing health inequalities To this end, stakeholders who have knowledge about and power over various determinants of health could be involved in identifying and defining a broader digital health dataset, one that makes explicit the systemic facilitators and barriers that affect how people live. J Participat Med 2020 | vol 12 | iss. 3 | e19586 | p. 3 (page number not for citation purposes)

Objective

Conclusions