Health systems are using AI to predict severe Covid-19 cases. But limited data could produce unreliable results
As the United States braces for a bleak winter, hospital systems across the country are ramping up their efforts to develop AI systems to predict how likely their Covid-19 patients are to fall severely ill or even die. Yet most of the efforts are being developed in siloes and trained on limited datasets, raising crucial questions about their reliability.
Dozens of institutions and companies — including Stanford, Mount Sinai, and the electronic health records vendors Epic and Cerner — have been working since the spring on models that are essentially designed to do the same thing: crunch large amounts of patient data and turn out a risk score for a patient’s chances of dying or needing a ventilator.
