| Titre : | Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California. (2011) |
| Auteurs : | Bruce-Y LEE ; Taliser-R AVERY ; Rachel-R BAILEY ; Donald-S BURKE ; Chung-Chou CHANG ; Stephen EUBANK ; Susan-S HUANG ; Sarah-M MCGLONE ; Richard PLATT ; Yeohan SONG ; Diane-K WAGENER ; Department of Population Medicine. Harvard Medical School. Boston. MA. USA ; Departments of Medicine and Biomedical Informatics. University of Pittsburgh School of Medicine. Pittsburgh. USA ; Public Health Computational and Operations Research Group. Department of Epidemiology. University of Pittsburgh Graduate School of Public Health. Pittsburgh. PA. USA |
| Type de document : | Article |
| Dans : | American journal of public health (vol. 101, n° 4, 2011) |
| Pagination : | 707-713 |
| Langues: | Anglais |
| Mots-clés : | Relation sociale ; Homme ; Malade ; Hôpital ; Amérique ; Amérique du Nord |
| Résumé : | [BDSP. Notice produite par INIST-CNRS Hr7R0xs8. Diffusion soumise à autorisation]. 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. |
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