Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling.

ABSTRACT:

Objectives

To develop and test a novel machine learning approach for monitoring impact of computerized clinical decision support (CDS) tools on clinicians' electronic health record (EHR) activities.

Materials and methods

Our CDS monitoring approach leverages topic modeling, a latent-variable statistical machine learning method, to infer health providers' EHR activities from EHR audit logs. We applied this approach to monitor the impact of a tobacco cessation support CDS tool newly implemented in 5 cancer clinics (2018-2021). We trained the topic model on EHR audit log data from 3445 encounters (pre-CDS-implementation: 1734, post-CDS-implementation: 1711) for patients with active smoking status. The number of topics was automatically determined based on within-topic coherence

SUBMITTER: Chen J 

PROVIDER: S-EPMC12536917 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature