A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews. (10th June 2014)

• Record Type: Journal Article
• Authors: Wallace, Byron C; Paul, Michael J; Sarkar, Urmimala; Trikalinos, Thomas A; Dredze, Mark
• Is Part Of: Journal of the American Medical Informatics Association. Volume 21:Number 6(2014:Nov.)
• Page Start: 1098
• Page End: 1103
• Publication Date: 2014-06-10
• Subjects: social media -- physician reviews -- topic modeling -- natural language processing; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1136/amiajnl-2014-002711
• Languages: English
• ISSNs: 1067-5027

Abstract

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.