Concierge Care and Patient Reviews
Concierge medicine, Interpersonal skills, Machine learning, Patient reviews, Sentiment analysis
We examine how patient numerical ratings and specific words in written reviews of family physicians and internists in the states of California and Florida differ based upon concierge doctor status. Data are drawn from Healthgrades.com, one of the largest providers of online reviews, and a machine‐learning sentiment analysis is used to determine the predictors of concierge status and numerical patient ratings. We find that reviews of concierge doctors are more likely to contain technical words associated with health care, such as “staff” and “office,” compared with traditional physicians. In contrast, interpersonal bedside‐manner words, like “listen” or “concerns,” are most likely in reviews for nonconcierge doctors. We further determine that, whereas interpersonal words exhibit both positive and negative effects on numerical ratings, technical terms seem to primarily correlate negatively with patient scores for all doctors. The present work represents a first step towards understanding the measures of quality of care that relate with the patient experience, and in particular with respect to the growing field of concierge medicine. It is also the first attempt we are aware of that employs sentiment analysis in this context.
Nemzer, L. R., & Neymotin, F. (2020). Concierge Care and Patient Reviews. Health Economics, 1 - 10. https://doi.org/10.1002/hec.4028. Retrieved from https://nsuworks.nova.edu/cnso_chemphys_facarticles/274