🤖 AI Summary
Electronic health record (EHR) audit logs contain rich, multidimensional information about clinical activities, yet lack a unified modeling framework. This work proposes a “multi-axis trace” perspective that simultaneously associates each logged action with clinician behavior, patient care trajectories, team collaboration patterns, and repetitive workflow structures, thereby uncovering its multifaceted clinical semantics. Building on this insight, we develop a representation learning framework that preserves the multi-axis structure by pretraining a foundation model directly on raw audit log streams to learn general-purpose representations. The resulting approach establishes a unified data representation and evaluation paradigm applicable to diverse downstream tasks, including clinical workload analysis, patient outcome prediction, team coordination assessment, and workflow modeling.
📝 Abstract
Electronic health record audit logs record timestamped actions through which clinical work is carried out. Generated as operational metadata, they now support research on clinician effort, patient outcomes, care-team coordination, and workflow structure. This Perspective explains that breadth by articulating audit logs as multi-axial event streams and drawing implications for representation learning, evaluation, and governance. Each logged action belongs simultaneously to multiple clinically meaningful relations: a clinician's work, a patient's trajectory, a team's activity, and a recurring workflow. This structure motivates foundation-model pretraining to learn reusable representations over the raw stream. Reading audit logs as multi-axial traces specifies what such representations must preserve, how their value should be tested, and how their use should be governed.