{
  "abstract": "Healthcare facilities are interconnected via patient transfers, facilitating pathogen spread. We systematically reviewed studies reconstructing patient-sharing networks. Following PRISMA 2020 guidelines and a preregistered protocol (CRD42024559127), we searched PubMed, Scopus and IEEE Xplore up to October 1, 2025. We included articles presenting networks of at least three healthcare facilities or units connected by observed patient transfers, excluding those restricted to specific patient groups. Seventy-nine articles were included from 5,721 screened, describing 50 distinct networks published across multiple disciplines. Overall confidence in study findings, assessed through a modified QuADS tool, was good (mean total score of 21/33), although precision in the description of underlying patient transfer data was often limited. Most networks were reconstructed in Europe and North America. The majority were restricted to hospitals, with nursing homes and other healthcare facilities rarely integrated, and 10 (20%) used temporal network approaches. While most articles reported descriptive statistics and network metrics, they focused mainly on density and degree (and its variants), with other metrics only rarely calculated. Only 27 (35%) articles developed mathematical models of pathogen transmission, including 12 that simulated public health interventions. Overall, this review highlights the value of patient-sharing networks to characterize healthcare connectivity and understand pathogen spread. Clearly articulating the methodological assumptions underlying network reconstruction and tailoring them to specific research questions is key to fully leveraging the reconstructed networks for public health purposes. In particular, including a broader range of facility types and more temporal modelling can help develop more realistic models of pathogen spread over these networks. This review was funded by the French National Research Agency (ANR 22-PAMR-0003). Author summary: Healthcare systems are strongly connected through patient movements between care facilities. While these transfers are essential for patient care, they can also facilitate the spread of pathogens across healthcare settings. In recent years, digital patient transfer data have been used to map healthcare networks, with the aim of better understanding care organization and infection risks. However, no review has examined all studies reconstructing patient-sharing networks between healthcare facilities. In this study, we review 79 research articles that built such networks using routine patient transfer data. Together, these studies described 50 distinct networks from multiple countries, mainly in Europe and North America. We find that most networks focused on hospitals only, overlooking other facilities such as nursing homes and rehabilitation centers. Moreover, most studies relied on static representations that fail to capture the temporal dynamics of patient movements. About a third of the studies also developed a mathematical model of pathogen spread on these networks, enabling simulations to assess infection control strategies. Overall, our findings show that patient transfer networks are a promising yet underused digital tool for understanding healthcare systems. Expanding their scope and making better use of temporal data could strengthen surveillance and support more effective infection prevention.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Elise Hodbert"
    },
    {
      "name": "Pascal Crépey"
    },
    {
      "name": "Pauline Clin"
    },
    {
      "name": "Victor Renault"
    },
    {
      "name": "Gabriel Birgand"
    },
    {
      "name": "Laura Temime"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/df79jxt002eq7.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001718",
  "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001718",
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  "language": "en",
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  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2725|Infection Control in Healthcare"
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      "plos",
      "/Computer and information sciences/Network analysis"
    ],
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      "plos",
      "/Medicine and health sciences/Health care/Health care facilities"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Health care facilities/Hospitals"
    ],
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      "plos",
      "/Medicine and health sciences/Health care/Health care providers/Allied health care professionals"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Europe"
    ],
    [
      "plos",
      "/People and places/Geographical locations/North America"
    ],
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      "plos",
      "/Research and analysis methods/Research assessment/Systematic reviews"
    ]
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  "openalex": "W7213282488",
  "pdf_url": "https://hal.science/hal-05752506/document",
  "pmcid": null,
  "pmid": "42743144",
  "published": "2026-09-15",
  "publisher": "PLOS",
  "ris": "https://research.draconitic.com/paper/df79jxt002eq7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:35Z",
      "native_id": "hal-05752506",
      "source": "hal",
      "url": "https://hal.science/hal-05752506v2"
    },
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pdig.0001718",
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  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "plos:systematic reviews",
      "subfield": "clinical-trials"
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    {
      "field": "medicine",
      "origin": "hal:1.sdv.spee",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Reconstructing healthcare networks from patient transfer data: A systematic review",
  "title_text": "Reconstructing healthcare networks from patient transfer data: A systematic review",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/df79jxt002eq7",
  "venue": "PLOS Digital Health",
  "versions": []
}