Punished for Success? A Natural Experiment of Displaying Clinical Hospital Quality on Review Platforms

Lianlian (Dorothy) Jiang (), Jinghui (Jove) Hou (), Xiao Ma () and Paul A. Pavlou ()
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Lianlian (Dorothy) Jiang: Decision and Information Science, C. T. Bauer College of Business, University of Houston, Houston, Texas 77004
Jinghui (Jove) Hou: Decision and Information Science, C. T. Bauer College of Business, University of Houston, Houston, Texas 77004
Xiao Ma: Decision and Information Science, C. T. Bauer College of Business, University of Houston, Houston, Texas 77004
Paul A. Pavlou: Decision and Information Science, C. T. Bauer College of Business, University of Houston, Houston, Texas 77004

Information Systems Research, 2025, vol. 36, issue 1, 285-306

Abstract: The healthcare market faces severe information asymmetry; patients struggle to evaluate the quality of hospitals and make informed decisions about their healthcare. Review platforms (e.g., Yelp) have begun to display the clinical quality of hospitals (alongside consumer reviews) to inform patients about hospital selection. In 2017 and 2019, Yelp introduced a feature with clinical measures of maternity care for hospitals that deliver babies in select markets. We study how clinical quality measures displayed on Yelp—especially for those (clinically) high-quality hospitals—influence subsequent patients’ ratings of hospitals. Our difference-in-differences estimation shows that when clinical quality measures are displayed, high-quality hospitals are surprisingly punished with lower subsequent ratings on Yelp, especially hospitals with low staffing capacity. This novel finding is consequential for hospitals as patient dissatisfaction can jeopardize the federal funding that hospitals receive ( CMS.gov ). To tease out the underlying mechanism, we queried SafeGraph’s precise foot traffic data, and we observed a significant patient surge for hospitals that have high maternity care clinical scores displayed on Yelp. We used transfer deep learning to show that because of the patient surge, (only) hospitals with high maternity scores that were short staffed received significantly more negative patient reviews and more complaints about key hospital service areas, thus driving patient dissatisfaction and lower ratings. We contribute to theory and practice by elucidating the role of publicly displaying clinical quality measures in patient (dis-)satisfaction with hospitals.

Keywords: clinical quality measures; online consumer ratings; hospital selection; natural experiment; transfer deep learning; difference in differences; hospital staffing capacity; natural language processing; SafeGraph (search for similar items in EconPapers)
Date: 2025
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