Advocacy: Addressing health inequities with AI-powered populations

With the right use of data, the potential for improving population health is enormous. We can move from nations where equity is an afterthought to ones that rebuild equity into their foundations. COVID-19 hit the world like a tropical storm, bringing the greatest devastation to people on lower socioeconomic ground. In its wake, these populations were left with the most job losses, least access to care, and worst health outcomes, widening the socioeconomic divide. The world now clearly recognises social injustice as a cause-and-effect scenario. At the beginning of the pandemic, we could predict regions within the United States where a particular subset of the population (Medicare fee-for-service enrollees) were at highest risk of COVID-19 mortality. A year later, vaccine data revealed the same high-risk regions had lower vaccine rates. Even with accurate forecasting, the most at-risk people often had less access to care, resulting in unnecessary loss of life. COVID-19 demonstrated systemic failure of evidence-based action. Little progress has been made to reduce known disparities, despite vast amounts of spending over decades. As we look beyond the pandemic, societies worldwide are embracing the dual goals of improving population health and reducing inequities. Big data analytics can help achieve both. But we need to look at different data, in a different way.