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

Journal: Journal of nursing care quality

Published Date: Jan 1, 2020

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.

Authors

Heather Brom

Center for Health Outcomes and Policy Research, and University of Pennsylvania Leonard Davis Institute of Health Economics, University of Pennsylvania School of Nursing, Philadelphia (Drs Brom and Brooks Carthon); and Biostatistics Evaluation Collaboration Consultation Analysis (BECCA) Lab, University of Pennsylvania School of Nursing, Philadelphia (Messrs Ikeaba and Chittams).
J Margo Brooks Carthon
Uchechukwu Ikeaba
Jesse Chittams

Keywords

Aged Data Analysis Electronic Health Records Female Humans Machine Learning Male Middle Aged Nursing Care Patient Readmission Retrospective Studies Risk Factors Risk Management

External Resources

View on PubMed Access via DOI PubMed (31136529)

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

Abstract

Authors

Keywords

External Resources

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