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

35 Pages Posted: 12 May 2016 Last revised: 31 Aug 2017

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 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.

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
New York, NY NY 10012
United States

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