Physicians often query a patient’s electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average, more than eight minutes.

The more time physicians must spend navigating an oftentimes clunky EHR interface, the less time they have to interact with patients and provide treatment.

Researchers have begun developing machine-learning (ML) models that can streamline the process by automatically finding information physicians need in an EHR. However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions. Existing models struggle to generate authentic questions—those that would be asked by a human doctor—and are often unable to successfully find correct answers.

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