Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models

We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data for the entire context of a patient's hospitalization to find surprising events. Context enables flagging anomalous events that rule-based approaches would consider within a normal range. We demonstrate that the events our model flags are significantly more useful than average events for predicting downstream patient outcomes and show that a fraction of events we identify as unsurprising can be safely dropped without an adverse impact on performance. Finally, we show how informativeness can help interpret the predictions of prognostic models trained on foundation model-derived representations.