Modeling physician variability to prioritize relevant medical record information.

ABSTRACT:

Objective

Patient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.

Materials and methods

Critical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their rel

SUBMITTER: Tajgardoon M 

PROVIDER: S-EPMC7886572 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature