Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods

Abstract Background Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between th...

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Autores principales:	Jan Chrusciel, Adrien Le Guillou, Eric Daoud, David Laplanche, Sandra Steunou, Marie-Caroline Clément, Stéphane Sanchez
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/c723b293b4ac420089a8c6a1692cf2ae
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spelling	oai:doaj.org-article:c723b293b4ac420089a8c6a1692cf2ae2021-11-21T12:06:09ZMaking sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods10.1186/s12913-021-07215-41472-6963https://doaj.org/article/c723b293b4ac420089a8c6a1692cf2ae2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12913-021-07215-4https://doaj.org/toc/1472-6963Abstract Background Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network. Methods Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days). Results Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 – 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%). Conclusions The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.Jan ChruscielAdrien Le GuillouEric DaoudDavid LaplancheSandra SteunouMarie-Caroline ClémentStéphane SanchezBMCarticlePublic aspects of medicineRA1-1270ENBMC Health Services Research, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Public aspects of medicine RA1-1270
spellingShingle	Public aspects of medicine RA1-1270 Jan Chrusciel Adrien Le Guillou Eric Daoud David Laplanche Sandra Steunou Marie-Caroline Clément Stéphane Sanchez Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
description	Abstract Background Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network. Methods Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days). Results Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 – 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%). Conclusions The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.
format	article
author	Jan Chrusciel Adrien Le Guillou Eric Daoud David Laplanche Sandra Steunou Marie-Caroline Clément Stéphane Sanchez
author_facet	Jan Chrusciel Adrien Le Guillou Eric Daoud David Laplanche Sandra Steunou Marie-Caroline Clément Stéphane Sanchez
author_sort	Jan Chrusciel
title	Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
title_short	Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
title_full	Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
title_fullStr	Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
title_full_unstemmed	Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
title_sort	making sense of the french public hospital system: a network-based approach to hospital clustering using unsupervised learning methods
publisher	BMC
publishDate	2021
url	https://doaj.org/article/c723b293b4ac420089a8c6a1692cf2ae
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Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods

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