Predicting inpatient pharmacy order interventions using provider action data.

ABSTRACT:

Objective

The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient's medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new sources of inefficiencies.

Materials and methods

Data on providers' actions in the EHR system and pharmacy orders were collected over a 2-week period in a major metropolitan hospital system. A classification model was then built to identify orders requiring pharmacist intervention. W

SUBMITTER: Balestra M 

PROVIDER: S-EPMC8490931 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature