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

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.

Materials and methods

We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, l

SUBMITTER: Cronin RM 

PROVIDER: S-EPMC5546247 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature