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A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews.


ABSTRACT: Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60,000 such reviews with a state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess whether model output improves correlation with state-level measures of healthcare. We report both qualitative and quantitative results. Model output correlates with state-level measures of quality healthcare, including patient likelihood of visiting their primary care physician within 14?days of discharge (p=0.03), and using the proposed model better predicts this outcome (p=0.10). We find similar results for healthcare expenditure. Generative models of text can recover important information from online physician reviews, facilitating large-scale analyses of such reviews.

SUBMITTER: Wallace BC 

PROVIDER: S-EPMC4215053 | biostudies-literature | 2014 Nov-Dec

REPOSITORIES: biostudies-literature

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A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews.

Wallace Byron C BC   Paul Michael J MJ   Sarkar Urmimala U   Trikalinos Thomas A TA   Dredze Mark M  

Journal of the American Medical Informatics Association : JAMIA 20140610 6


Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60,000 such reviews with a state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess wheth  ...[more]

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