Overview

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This session contains two 40 minute panels.

As health researchers who have been rigorously studying and using synthetic data, our goal has been to optimise data accessibility and utility for research, addressing existing barriers to using clinical data while preserving patient privacy. To advance science, we see the need to accelerate time-to-results, optimise data science methodologies, and facilitate knowledge sharing. Traditional approaches to protecting privacy of clinical data present challenges to achieving these goals: Human-subject research requires often-laborious oversight processes involving institutional review boards or ethics committees, which achieve the goal of protecting patients’ interests but often increase time-to-insight; large amounts of potentially sensitive data are needed for development and testing of artificial intelligence algorithms; and the sensitive, restricted, and proprietary nature of clinical data often limits their collaborative use between institutions or across borders.

The COVID-19 pandemic has ushered in a paradigm shift in healthcare toward digital transformation and brought the opportunity to rethink the value of these initiatives. Virtual care has been expanded worldwide to remotely monitor and treat patients and keep their distance. Despite the widespread implementation and use of virtual care, little is known about how it contributes to the provision of value-based care. This panel will discuss various requirements of virtual care models to demonstrate value. The panel will present implementation experiences of virtual care since the beginning of the COVID-19 pandemic in four countries. Finally, the panel will address the lessons learned and the implications for designing value-based virtual care models and discuss these issues with the audience.

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