Format

Send to

Choose Destination
See comment in PubMed Commons below
AMIA Annu Symp Proc. 2017 Feb 10;2016:524-533. eCollection 2016.

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

Author information

1
Westminster College, Salt Lake City, UT.
2
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
3
Director of Strategic Initiatives, University of Utah, Salt Lake City, UT.

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.

PMID:
28269848
PMCID:
PMC5333198
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Support Center