@Article{info:doi/10.2196/jmir.2721,
author="Greaves, Felix
and Ramirez-Cano, Daniel
and Millett, Christopher
and Darzi, Ara
and Donaldson, Liam",
title="Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online",
journal="J Med Internet Res",
year="2013",
month="Nov",
day="01",
volume="15",
number="11",
pages="e239",
keywords="Internet; patient experience; quality; machine learning",
abstract="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. ",
issn="14388871",
doi="10.2196/jmir.2721",
url="http://www.jmir.org/2013/11/e239/",
url="https://doi.org/10.2196/jmir.2721",
url="http://www.ncbi.nlm.nih.gov/pubmed/24184993"
}