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Cancer Med. 2018 Dec;7(12):5901-5909. doi: 10.1002/cam4.1821. Epub 2018 Nov 15.

Surgeon peer network characteristics and adoption of new imaging techniques in breast cancer: A study of perioperative MRI.

Author information

1
Yale University School of Medicine, New Haven, Connecticut.
2
Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale Cancer Center and Yale School of Medicine, New Haven, Connecticut.
3
Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
4
Section of Cardiology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
5
Health Research & Educational Trust, Chicago, Illinois.
6
Johns Hopkins School of Medicine, Baltimore, Maryland.
7
Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
8
Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut.
9
Department of Sociology, Yale University, New Haven, Connecticut.
10
Yale Institute for Network Science and Human Nature Lab, Yale University, New Haven, Connecticut.
11
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
12
Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut.
13
Department of Surgery, Yale School of Medicine, New Haven, Connecticut.
14
Yale Cancer Center, New Haven, Connecticut.
15
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut.

Abstract

BACKGROUND:

Perioperative MRI has disseminated into breast cancer practice despite equivocal evidence. We used a novel social network approach to assess the relationship between the characteristics of surgeons' patient-sharing networks and subsequent use of MRI.

METHODS:

We identified a cohort of female patients with stage 0-III breast cancer from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. We used claims data from these patients and non-cancer patients from the 5% Medicare sample to identify peer groups of physicians who shared patients during 2004-2006 (T1). We used a multivariable hierarchical model to identify peer group characteristics associated with uptake of MRI in T2 (2007-2009) by surgeons who had not used MRI in T1.

RESULTS:

Our T1 sample included 15 149 patients with breast cancer, treated by 2439 surgeons in 390 physician groups. During T1, 9.1% of patients received an MRI; the use of MRI varied from 0% to 100% (IQR 0%, 8.5%) across peer groups. After adjusting for clinical characteristics, patients treated by surgeons in groups with a higher proportion of primary care physicians (PCPs) in T1 were less likely to receive MRI in T2 (OR = 0.81 for 10% increase in PCPs, 95% CI = 0.71, 0.93). Surgeon transitivity (ie, clustering of surgeons) was significantly associated with MRI receipt (P = 0.013); patients whose surgeons were in groups with higher transitivity in T1 were more likely to receive MRI in T2 (OR = 1.29 for 10% increase in clustering, 95% CI = 1.06, 1.58).

CONCLUSION:

The characteristics of a surgeon's peer network are associated with their patients' subsequent receipt of perioperative MRI.

KEYWORDS:

Medicare; breast cancer; diagnostic imaging; magnetic resonance imaging; social networking

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