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. 2011 Apr;101(4):707-13.
doi: 10.2105/AJPH.2010.202754. Epub 2011 Feb 17.

Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California

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Free PMC article

Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California

Bruce Y Lee et al. Am J Public Health. .
Free PMC article

Erratum in

  • Am J Public Health. 2011 Oct;101(10):1815

Abstract

Objectives: We applied social network analyses to determine how hospitals within Orange County, California, are interconnected by patient sharing, a system which may have numerous public health implications.

Methods: Our analyses considered 2 general patient-sharing networks: uninterrupted patient sharing (UPS; i.e., direct interhospital transfers) and total patient sharing (TPS; i.e., all interhospital patient sharing, including patients with intervening nonhospital stays). We considered these networks at 3 thresholds of patient sharing: at least 1, at least 10, and at least 100 patients shared.

Results: Geographically proximate hospitals were somewhat more likely to share patients, but many hospitals shared patients with distant hospitals. Number of patient admissions and percentage of cancer patients were associated with greater connectivity across the system. The TPS network revealed numerous connections not seen in the UPS network, meaning that direct transfers only accounted for a fraction of total patient sharing.

Conclusions: Our analysis demonstrated that Orange County's 32 hospitals were highly and heterogeneously interconnected by patient sharing. Different hospital populations had different levels of influence over the patient-sharing network.

Figures

FIGURE 1
FIGURE 1
Sociograms depicting patient sharing among all hospitals within 1 year of discharge: 2005–2006, Orange County, CA. Note. Hospital patients were admitted during the 2005 calendar year. Diagrams on the left side are binary sociograms of the total patient-sharing network at 3 different patient-sharing thresholds: ≥ 1 patient (a), ≥ 10 patients (c), and ≥ 100 patients (e). Diagrams on the right side are binary sociograms for the uninterrupted patient-sharing network at the same 3 patient-sharing thresholds (b, d, and f, respectively).
FIGURE 2
FIGURE 2
Ego networks depicting 1-step connections of a relatively isolated hospital (a), moderately connected hospital (b), and expansively connected hospital (c): 2005–2006, Orange County, CA. Note. Results shown are for the total patient-sharing network at the ≥ 100 patients threshold. The star represents the ego hospital.

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