Clinician burnout, driven in part by the increasing burden of EHR documentation, is a significant threat to healthcare quality. This talk will introduce SPEER, a novel approach to automatically generating clinically useful hospital-course summaries from raw clinical notes. The SPEER method leverages the power of Large Language Models (LLMs) while addressing key challenges in clinical summarization, such as identifying salient information from lengthy records and ensuring the accuracy of generated summaries. We will also discuss the state of the art in automated evaluation methods and implications of summarization from a human-centered AI standpoint.
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