Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.

ABSTRACT:

Background

The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department.

Methods

The CURB-65 severity score and HEART score were randomly selected from a list of the top emergency medicine CDRs. Emergency depa

SUBMITTER: Perry WM 

PROVIDER: S-EPMC6029277 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature