Evidence map›Paper›PMID 34789235›Full record

ArticleBMC health services research2021

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

Jan Chrusciel, Adrien Le Guillou, Eric Daoud, David Laplanche, Sandra Steunou, Marie-Caroline Clément, Stéphane Sanchez

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Jan ChruscielPôle Territorial Santé Publique et Performance, Centre Hospitalier de Troyes, F-10000, Troyes, France. jan.chrusciel@hcs-sante.fr.
Adrien Le GuillouPôle Recherche et Santé Publique, Centre Hospitalier Universitaire de Reims, 51100, Reims, France.
Eric DaoudResidual Tumor & Response to Treatment Laboratory, RT2Lab, INSERM, U932 Immunity and Cancer, Institut Curie, Université Paris, 75005, Paris, France.
David LaplanchePôle Territorial Santé Publique et Performance, Centre Hospitalier de Troyes, F-10000, Troyes, France.
Sandra SteunouDepartment of Data, Agence Technique d'Information sur l'Hospitalisation, 69003, Lyon, France.
Marie-Caroline ClémentDepartment of Classifications in Healthcare, Medical Information and Financing Models, Agence Technique d'Information sur l'Hospitalisation, 75012, Paris, France.
Stéphane SanchezPôle Territorial Santé Publique et Performance, Centre Hospitalier de Troyes, F-10000, Troyes, France.
Centre Hospitalier de Troyes · FRCentre Hospitalier Universitaire de Reims · FRInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospitals 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.

methodsData 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).

resultsThree 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%).

conclusionsThe 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.

Indexed as

Hospitals, PublicUnsupervised Machine LearningCluster AnalysisHealth ServicesHumansPopulation Groups

Identifiers

PMID34789235
PMCPMC8600901
OpenAlexW3213463378

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.