Physician Connectedness and Referral Choice*
| Published date | 01 December 2023 |
| Author | Eunhae Shin |
| Date | 01 December 2023 |
| DOI | http://doi.org/10.1111/obes.12525 |
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 85, 6 (2023) 0305-9049
doi: 10.1111/obes.12525
Physician Connectedness and Referral Choice*
EUNHAE SHIN
Mathematica, 505 14th Street, Suite 800, Oakland, California 94612, USA
(e-mail: EShin@mathematica.org)
Abstract
This study examines the effects of social network structure of intermediaries in health care,
namely referring physicians, upon the specialty treatment choices of patients in the United
States. The social network of a referring physician is identified by the patient-sharing
pattern in Medicare claims data, and the following three measures are employed as key
explanatory variables: (1) number of physicians connected (adjusted degree); (2) tightness
of the network (clustering coefficient); and (3) influence of individual physicians in the
network (eigenvector centrality). The results of discrete-choice demand models suggest
that if patients are referred by a physician who is a more important player in their social
networks (i.e. eigenvector centrality is higher), the patient has a higher chance of choosing
a surgeon of better quality.
I. Introduction
Asymmetric information in health care is one of the most well-known examples of the
market failure (Arrow, 1963). In theory, patients should have sufficient information to
identify provider quality, which will in turn encourage providers to improve quality to
increase demand. This theory often fails to hold in the market for health care because,
unlike the market for typical goods and services, there are not many easily accessible
measures of health care quality. Even if there are such measures, they are not readily
interpretable by patients in practice. Thus, when patients need specialized care, they often
consult with a referring physician who is supposed to have better knowledge on the quality
JEL Classification numbers: I11, I13, I18.
*This study has benefited tremendously from the invaluable support and advice of Dana Goldman, Alice Chen,
and John Romley. I also thank Jeffrey Nugent, Darius Lakdawalla, Rebecca Myerson, Michael Leung, Paulina
Oliva, Joel Segel, Bo Shi, Toshiaki Iizuka, Laura Henkhaus, Ambuj Dewan, and seminar participants at the 2018
American Society of Health Economists (ASHEcon) Conference and the 2018 AcademyHealth Health Economics
Interest Group Meeting. The author is grateful to the editor, Climent Quintana-Domeque, and three anonymous
referees for helpful comments and suggestions that have led to substantial improvement of the paper. Jean Roth
and Mohan Ramanujan at the National Bureau of Economic Research and Patricia St. Clair and Jillian Wallis at the
Schaeffer Center’s Data Core provided assistance with the Medicare data. I gratefully acknowledge funding from
the USC Provost’s Ph.D. Fellowship, the Leonard D. Schaeffer Center Predoctoral Fellowship, the USC Graduate
School Summer Research Grant, and the USC Graduate School Advanced Fellowship. Research reported in this
publication was supported by the National Institute on Aging of the National Institutes of Health under Award
Number P30AG024968. The content is solely the responsibility of the authors and does not necessarily represent
the official views of the National Institutes of Health.
1238
©2022 Oxford University and John Wiley & Sons Ltd.
Physician Connectedness and Referral Choice1239
of specialists. The information asymmetry problem still exists between specialists and
referring doctors, however, due to the intrinsic nature of health care that provides a large
informational advantage to performing agents.
Studies examining the determinants of physician referrals to date have mainly focused
on the role of patient characteristics (Mukamel, Weimer and Mushlin, 2006; Campbell
et al., 2011), physician demographics (Zeltzer, 2020), institutional or organizational
features on the supply side (Iversen and Lur˚
as, 2000; Marinoso and Jelovac, 2003;
Nakamura, 2010; Carlin, Feldman and Dowd, 2016), and government interventions
including report cards (Schneider and Epstein, 1996; Epstein, 2010). On the other hand,
economists have increasingly paid attention to the role of friends and neighbours as
a channel of spreading important news and information. For instance, social networks
have been studied as a mechanism that explains unequal employment opportunities (Gee,
Jones and Burke, 2017), insurance adoption (Duflo and Saez, 2003; Cai, De Janvry
and Sadoulet, 2015; Grossman and Khalil, 2020), variations in productivity (Fernandez,
Castilla and Moore, 2000;Ductor,2015), decision-making in the housing market (Bailey
et al., 2018), and the extent of innovation and diffusion (Munshi, 2004; Bandiera and
Rasul, 2006; Conley and Udry, 2010; Banerjee et al., 2013). However, despite the existing
studies suggesting the importance of word of mouth and consumers’ social networks in
understanding health care demand (Dafny and Dranove, 2008; Epstein, 2010; Chen, 2011),
little is known about the effects of the network structure of referring physicians on their
choice of specialty care providers.
This study examines whether referring physicians who have better connections in
their social networks can lead their patients to better-performing specialty care providers.
Using a 20% random sample of Medicare beneficiaries undergoing coronary artery
bypass graft (CABG) included in the Medicare Part B claims files (2008– 13), this study
identifies information on cardiac surgeons and referring physicians (mostly cardiologists
or primary care physicians). Building on the existing literature, the social networks of
referring physicians are defined by their patient-sharing patterns with other physicians in
Medicare claims data, and the following three measures are employed as key explanatory
variables: (1) number of physicians connected (adjusted degree); (2) tightness of the
network (clustering coefficient); and (3) influence of individual physicians in the network
(eigenvector centrality). The results of discrete-choice demand models suggest that if
patients are referred by a physician who is a more important player in their social
networks (i.e. eigenvector centrality is higher), the patient has a higher chance of choosing
a surgeon of better quality.
This study contributes to three strands of the literature. First, this paper examines
an important yet largely understudied factor that explains physician referral behaviour,
namely, the social connectedness of referring physicians. In fact, the results indicate that
the interpersonal characteristics of primary care providers are an important determinant of
physician referral decisions, and therefore of the demand for specialty health care services.
Second, the paper adds to recent literature that examines the effects of information
obtained through person-to-person interactions on consumer decision-making. The result
that better-connected consumers make a more informed choice is consistent with the
findings of Cai et al. (2015), demonstrating that consumers having more friends purchase
insurance with a lower premium, and the study by Bailey et al. (2018), which shows
©2022 Oxford University and John Wiley & Sons Ltd.
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