Facebook Hospital Reviews: Automated Service Quality Detection and Relationships with Patient Satisfaction

ABSTRACT As patient satisfaction is heavily linked to their choice of provider and medical outcomes, hospital administrations routinely consider a bevy of factors to improve patient satisfaction. These considerations are complex, so targeting the most important areas for improvement is challenging. However, consumers’ online reviews of their hospital experience provide a vital lens into the factors associated with their satisfaction. In this study, we use a large dataset of Facebook reviews to construct a taxonomy of potential service attributes that consumers discuss online. We find partial overlap between this taxonomy and prior works and more traditional survey measures; the specific mix of service attributes found in these reviews is unique. Next, we utilize regression modeling to determine which service attributes are most closely associated with star ratings, which we use to measure overall satisfaction. This study demonstrates that mentions of waiting times, treatment effectiveness, communication, diagnostic quality, environmental sanitation, and cost considerations tend to be most associated with patients’ overall ratings. Finally, we construct text analyses to rapidly detect consumers’ mentions of these service attributes in an automated manner. We derive a set of “smoke terms,” or terms especially prevalent in posts that mention specific service attributes. We find that these are generally non‐emotive terms, indicating limited utility of traditional sentiment analysis. Managerially, this information helps to prioritize the areas in greatest need of improvement. Additionally, generating smoke terms for each service attribute aids health care policy makers and providers in rapidly monitoring concerns and adjusting policies or resources to improve service.