The Interplay between Online Reviews and Physician Demand: An Empirical Investigation

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews have been providing additional information to patients when choosing their physicians. In this paper, we derive various service-quality proxies from online reviews and study the relationship between these quality proxies and physician demand. To do so, we study a unique data set from one of the leading appointment booking websites in the United States, that contains online physicians' appointments made over a five-month period, along with other online information. We propose a random coefficient choice model to characterize patient heterogeneity in physician choices, taking into account both numeric and textual user-generated content with text mining techniques. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis, waiting time, service time, insurance process, physician knowledge, and office environment. We incorporate these service features into our choice model, and find a statistically significant relationship between demand and four service features, namely, bedside manner, diagnosis, waiting time, and service time. We proceed with counterfactual experiments, and simulate the impact of proposed policy changes. We find that rating improvement is indeed important in increasing physician demand and patient utility. The maximum possible demand improvement by increasing ratings is 7.24%, and patient utility improvement is 5.01%. Moreover, we find policies with specific improvement of an operational process or platform design can increase demand and utility even further. Broadly speaking, this paper shows how to incorporate social media information into a choice model to derive relationships between operational factors in healthcare delivery and patient choices. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques with empirical operations management.