title: Understanding patient satisfaction with received healthcare services: A natural language processing approach.
abstract: Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments. Our annotation scheme consisted of 28 topics, with positive and negative sentiment. Within those 28 topics, the seven most frequent accounted for 63% of annotations. For automated topic classification, we developed vocabulary-based and Naive Bayes ' classifiers. For sentiment analysis, another Naive Bayes ' classifier was used. Finally, we used topic modeling to search for unexpected topics within negative comments. The seven most common topics were appointment access, appointment wait, empathy, explanation, friendliness, practice environment, and overall experience. The best F-measures from our classifier were 0.52(NB), 0.57(NB), 0.36(Vocab), 0.74(NB), 0.40(NB), and 0.44(Vocab), respectively. F- scores ranged from 0.16 to 0.74. The sentiment classification F-score was 0.84. Negative comment topic modeling revealed complaints about appointment access, appointment wait, and time spent with physician.
authors.affiliation: Westminster College, Salt Lake City, UT.
authors.forename: Kristina
authors.initials: K
authors.lastname: Doing-Harris
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
authors.forename: Danielle L
authors.initials: DL
authors.lastname: Mowery
authors.affiliation: Director of Strategic Initiatives, University of Utah, Salt Lake City, UT.
authors.forename: Chrissy
authors.initials: C
authors.lastname: Daniels
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
authors.forename: Mike
authors.initials: M
authors.lastname: Conway
mesh_terms: D001499:Bayes Theorem; D003695:Delivery of Health Care; D006801:Humans; D009323:Natural Language Processing; D010043:Outcome and Process Assessment, Health Care; D017060:Patient Satisfaction; D009626:Terminology as Topic