Summary of action entropy calculation process. Beginning with unprocessed audit logs, we converted selected fields into tokens, with each token representing an action name, ordinal patient ID, provider ID, or quantized time-delta bins. Tokens for any given session were broken up into groups of 1024 tokens before being used as training examples for a language model. The LM attempted to learn to predict the next token with the training objective of minimizing the cross-entropy of the predicted distribution of tokens. The input was shifted left by one (ie, next token prediction), end-of-sentence (EOS) tokens are encoded with 0s, and tokens past the end-of-sentence token are masked with −100s. Action entropy was extracted as the cross-entropy calculated for the action name token fields from the out-of-sample test set.