Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions.

ABSTRACT:

Background

Electronic health record-derived data and novel analytics, such as machine learning, offer promising approaches to identify high-risk patients and inform nursing practice.

Purpose

The aim was to identify patients at risk for readmissions by applying a machine-learning technique, Classification and Regression Tree, to electronic health record data from our 300-bed hospital.

Methods

We conducted a retrospective analysis of 2165 clinical encounters from August to October 2017 using data from our health system's data store. Classification and Regression Tree was employed to determine patient profiles predicting 30-day readmission.

Results

The 30-day readmission rate was 11.2% (n = 242). Classification and Regression Tree analysis revealed highest risk fo

SUBMITTER: Brom H 

PROVIDER: S-EPMC6874718 | biostudies-literature | 2020 Jan/Mar

REPOSITORIES: biostudies-literature