title: Assessing Quality of Care and Elder Abuse in Nursing Homes via Google Reviews.
abstract: BACKGROUND
  It is challenging to assess the quality of care and detect elder abuse in nursing homes, since patients may be incapable of reporting quality issues or abuse themselves, and resources for sending inspectors are limited.
  
  
  OBJECTIVE
  This study correlates Google reviews of nursing homes with Centers for Medicare and Medicaid Services (CMS) inspection results in the Nursing Home Compare (NHC) data set, to quantify the extent to which the reviews reflect the quality of care and the presence of elder abuse.
  
  
  METHODS
  A total of 16,160 reviews were collected, spanning 7,170 nursing homes. Two approaches were tested: using the average rating as an overall estimate of the quality of care at a nursing home, and using the average scores from a maximum entropy classifier trained to recognize indications of elder abuse.
  
  
  RESULTS
  The classifier achieved an F-measure of 0.81, with precision 0.74 and recall 0.89. The correlation for the classifier is weak but statistically significant: = 0.13,P <.001, and 95% confidence interval (0.10, 0.16). The correlation for the ratings exhibits a slightly higher correlation: = 0.15,P <.001. Both the classifier and rating correlations approach approximately 0.65 when the effective average number of reviews per provider is increased by aggregating similar providers.
  
  
  CONCLUSIONS
  These results indicate that an analysis of Google reviews of nursing homes can be used to detect indications of elder abuse with high precision and to assess the quality of care, but only when a sufficient number of reviews are available.
authors.affiliation: The MITRE Corporation.
authors.forename: Jared
authors.initials: J
authors.lastname: Mowery
authors.affiliation: The MITRE Corporation.
authors.forename: Amanda
authors.initials: A
authors.lastname: Andrei
authors.affiliation: The MITRE Corporation.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Le
authors.affiliation: The MITRE Corporation.
authors.forename: Jing
authors.initials: J
authors.lastname: Jian
authors.affiliation: The MITRE Corporation.
authors.forename: Megan
authors.initials: M
authors.lastname: Ward
keywords: geriatric nursing; natural language processing; patient safety; social media; supervised machine learning