%0 Journal Article
%@ 14388871
%I JMIR Publications Inc.
%V 15
%N 11
%P e239
%T Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
%A Greaves,Felix
%A Ramirez-Cano,Daniel
%A Millett,Christopher
%A Darzi,Ara
%A Donaldson,Liam
%+ Department of Primary Care and Public Health, Imperial College London, Charing Cross Hospital, London, W6 8RF, United Kingdom, 44 7866551172, fg08@imperial.ac.uk
%K Internet
%K patient experience
%K quality
%K machine learning
%D 2013
%7 01.11.2013
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. Objective: We attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care. Methods: We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. Results: There was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). Conclusions: The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys. 
%M 24184993
%R 10.2196/jmir.2721
%U http://www.jmir.org/2013/11/e239/
%U https://doi.org/10.2196/jmir.2721
%U http://www.ncbi.nlm.nih.gov/pubmed/24184993