How an AI tool for fighting hospital deaths actually worked in the real world
In November of 2018, a new deep-learning tool went online in the emergency department of the Duke University Health System. Called Sepsis Watch, it was designed to help doctors spot early signs of one of the leading causes of hospital deaths globally.
In November of 2018, a new deep-learning tool went online in the emergency department of the Duke University Health System. Called Sepsis Watch, it was designed to help doctors spot early signs of one of the leading causes of hospital deaths globally.
At first glance, this is an example of a major technical victory. Through careful development and testing, an AI model successfully augmented doctors’ ability to diagnose disease. But a new report from the Data & Society research institute says this is only half the story. The other half is the amount of skilled social labor that the clinicians leading the project needed to perform in order to integrate the tool into their daily workflows. This included not only designing new communication protocols and creating new training materials but also navigating workplace politics and power dynamics, as reported by the MIT Technology Review.
