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

32 Pages Posted: 12 May 2016 Last revised: 5 Oct 2020

See all articles by Yuqian Xu

Yuqian Xu

University of Illinois at Urbana-Champaign - College of Business

Mor Armony

New York University (NYU) - Department of Information, Operations, and Management Sciences

Anindya Ghose

New York University (NYU) - Leonard N. Stern School of Business

Date Written: August 8, 2016

Abstract

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the US. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment; and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6% to 12% improvement measured by MSE for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users' perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing.

Keywords: physician, patient choice, quality, social media, text mining, sentiment analysis, rating, review, operational characteristic, outpatient care, healthcare.

Suggested Citation

Xu, Yuqian and Armony, Mor and Ghose, Anindya, The Interplay between Online Reviews and Physician Demand: An Empirical Investigation (August 8, 2016). Available at SSRN: https://ssrn.com/abstract=2778664 or http://dx.doi.org/10.2139/ssrn.2778664

Yuqian Xu (Contact Author)

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

Mor Armony

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States
(212) 998-0291 (Phone)
(212) 995-4227 (Fax)

Anindya Ghose

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
1,222
Abstract Views
4,754
rank
19,980
PlumX Metrics