title: Use of sentiment analysis for capturing patient experience from free-text comments posted online.
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.
authors.affiliation: Department of Primary Care and Public Health, Imperial College London, London, United Kingdom. fg08@imperial.ac.uk.
authors.forename: Felix
authors.initials: F
authors.lastname: Greaves
authors.forename: Daniel
authors.initials: D
authors.lastname: Ramirez-Cano
authors.forename: Christopher
authors.initials: C
authors.lastname: Millett
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
authors.forename: Liam
authors.initials: L
authors.lastname: Donaldson
mesh_terms: D001185:Artificial Intelligence; D004739:England; D006801:Humans; D020407:Internet; D017060:Patient Satisfaction; D013222:State Medicine
keywords: Internet; machine learning; patient experience; quality