title: Automated Classification of Consumer Health Information Needs in Patient Portal Messages.
abstract: Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.
authors.affiliation: Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Robert M
authors.initials: RM
authors.lastname: Cronin
authors.affiliation: Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fabbri
authors.affiliation: Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.affiliation: Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Gretchen Purcell
authors.initials: GP
authors.lastname: Jackson
mesh_terms: D054626:Consumer Health Information; D057286:Electronic Health Records; D006295:Health Resources; D006301:Health Services Needs and Demand / Q000145:classification*; D006801:Humans; D009323:Natural Language Processing; D000072638:Patient Portals