RESUMO
BACKGROUND: Influenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. However, research is needed to determine the impact of Twitter users' misdiagnoses on surveillance estimates. OBJECTIVE: This study establishes the importance of Twitter users' misdiagnoses by showing that Twitter flu surveillance in the United States failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses. METHODS: Metrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using Twitter data in conjunction with OpenSextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. These metrics were tested for correlations with World Health Organization (WHO) positive specimen counts of flu from 2011 to 2014. RESULTS: Twitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. Enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the Pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. A rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses. CONCLUSIONS: Further research into factors affecting Twitter users' misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable Twitter flu surveillance systems to produce reliable estimates during atypical flu seasons.
RESUMO
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.
