aceA language model framework for sequence modeling of audit logs to characterize clinician-electronic health record interactions
Objective: Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated measures (eg, total time). We investigated how audit logs could be modeled using large language model (LLM) architectures to learn underlying workflow sequences during clinician-EHR interactions. Materials and methods: Using >295 million EHR-based audit log actions from inpatient settings spanning 2019 to 2024, we fine-tuned Llama-3-8B under 2 encodings: (1) symbolic (…
Next-action prediction accuracy of the word-based model declined in later out-of-sample periods, indicating temporal drift in the learned workflow patterns.
Accuracy of the word-based model declined across the later out-of-sample periods, so the learned patterns are subject to temporal drift.
Evidence
- Peer-reviewedJournal of the American Medical Informatics Association2026-10-12
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Truvace Impact Record TRV-2026-1423, v1: “A language model framework for sequence modeling of audit logs to characterize clinician-electronic health record interactions.” Truvace, 2026-10-15. /record/TRV-2026-1423 (accessed at citation time). sha256 9d774e317cfd5c19…
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