Understanding patient satisfaction with received healthcare services: A natural language processing approach

Kristina Doing-Harris, Danielle L. Mowery, Chrissy Daniels, Wendy W. Chapman, Mike Conway

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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.

Original languageEnglish
Pages (from-to)524-533
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
StatePublished - 2016
Externally publishedYes


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