TY  - JOUR
AU  - Greaves, Felix
AU  - Ramirez-Cano, Daniel
AU  - Millett, Christopher
AU  - Darzi, Ara
AU  - Donaldson, Liam
PY  - 2013
DA  - 2013/11/01
TI  - Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
JO  - J Med Internet Res
SP  - e239
VL  - 15
IS  - 11
KW  - Internet
KW  - patient experience
KW  - quality
KW  - machine learning
AB  - 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. 
SN  - 14388871
UR  - http://www.jmir.org/2013/11/e239/
UR  - https://doi.org/10.2196/jmir.2721
UR  - http://www.ncbi.nlm.nih.gov/pubmed/24184993
DO  - 10.2196/jmir.2721
ID  - info:doi/10.2196/jmir.2721
ER  -