Data-driven responses to the COVID-19 pandemic

Over the last two years, we have witnessed numerous data-driven public health interventions to respond to the COVID-19 pandemic.

From broad-sweeping measures such as stay-at-home orders and lockdowns, to vaccine prioritisation, identification of vulnerable groups, remote health consultations and the use of algorithms to predict outpatient management and illness severity, data has been at the core of public health.

However, a retrospective look shows not only was the UK healthcare system  ill-prepared to respond to an emergency of such scale, but also that the use of data and technologies during the pandemic has had unintended consequences, including the exacerbation of pre-existing inequalities in health outcomes for different demographic groups.

Watch back the event:

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As data-intensive applications are projected to gain a prominent role in the normal administration of and research on health and social care, many questions need to be addressed:

• How do we measure the impact of rapid data-driven interventions on health inequalities?
• How do we bake equity into infrastructure that is designed to respond to health emergencies?
• What role can data play in achieving a more equitable public health system?
• How do we set standards, while the deployment of data-driven interventions – such as risk-scoring, triaging, etc. – is accelerating?