How Can Health Systems Better Prepare for the Next Pandemic? Lessons Learned From the Management of COVID-19 in Quebec (Canada).

The magnitude of the COVID-19 pandemic challenged societies around our globalized world. To contain the spread of the virus, unprecedented and drastic measures and policies were put in place by governments to manage an exceptional health care situation while maintaining other essential services. The responses of many governments showed a lack of preparedness to face this systemic and global health crisis. Drawing on field observations and available data on the first wave of the pandemic (mid-March to mid-May 2020) in Quebec (Canada), this article reviewed and discussed the successes and failures that characterized the management of COVID-19 in this province. Using the framework of Palagyi et al. on system preparedness toward emerging infectious diseases, we described and analyzed in a chronologically and narratively way: (1) how surveillance was structured; (2) how workforce issues were managed; (3) what infrastructures and medical supplies were made available; (4) what communication mechanisms were put in place; (5) what form of governance emerged; and (6) whether trust was established and maintained throughout the crisis. Our findings and observations stress that resilience and ability to adequately respond to a systemic and global crisis depend upon preexisting system-level characteristics and capacities at both the provincial and federal governance levels. By providing recommendations for policy and practice from a learning health system perspective, this paper contributes to the groundwork required for interdisciplinary research and genuine policy discussions to help health systems better prepare for future pandemics.

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

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.

Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries.

The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some health system gaps, especially the reduction of global health inequalities in low- and middle-income countries (LMICs). However, because most AI-based health applications are developed and implemented in high-income countries, their use in LMICs contexts is recent and there is a lack of robust local evaluations to guide decision-making in low-resource settings. After discussing the potential benefits as well as the risks and challenges raised by AI-based health care, we propose five building blocks to guide the development and implementation of more responsible, sustainable, and inclusive AI health care technologies in LMICs.