A comparison of rule-based and machine learning approaches for classifying patient portal messages.

Journal: International journal of medical informatics

Published Date: Sep 1, 2017

Abstract

OBJECTIVE: Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care.

Authors

Robert M Cronin

Vanderbilt University Medical Center, Nashville, Tennessee.
Daniel Fabbri

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Joshua C Denny

Vanderbilt University, Nashville, TN.
S Trent Rosenbloom

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
Gretchen Purcell Jackson

Vanderbilt University Medical Center, Nashville, Tennessee.

Keywords

Bayes Theorem Communication Electronic Health Records Health Personnel Health Services Needs and Demand Humans Machine Learning Natural Language Processing Patient Portals

External Resources

View on PubMed Access via DOI PubMed (28750904)

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

Abstract

Authors

Keywords

External Resources

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