# Reconstructing healthcare networks from patient transfer data: a systematic review

Elise Hodbert, Pascal Crépey, Pauline Clin, Victor Renault, Gabriel Birgand, Laura Temime

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Elise Hodbert, Pascal Crépey, Pauline Clin, Victor Renault, Gabriel Birgand, et al. Reconstructing healthcare networks from patient transfer data: a systematic review. 2026. hal-05520973

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# Reconstructing healthcare networks from patient transfer data: a systematic review

Elise Hodbert

elise.hodbert@lecnam.net

Conservatoire National des Arts et Métiers

https://orcid.org/0009-0006-3498-5698

Pascal Crépey

EHESP

Pauline Clin

Technical University of Munich

Victor Renault

CHU Nantes

Gabriel Birgand

CHU Nantes

Laura Temime

Conservatoire National des Arts et Métiers

## Systematic Review

**Keywords:** patient-sharing networks, healthcare networks, patient transfers, healthcare-associated infections, social network analysis, systematic review

**Posted Date:** January 29th, 2026

**DOI:** https://doi.org/10.21203/rs.3.rs-8702592/v1

**License:** This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

**Additional Declarations:** The authors declare no competing interests.
## Abstract

Healthcare facilities are interconnected via patient transfers, facilitating pathogen spread. We systematically reviewed studies reconstructing patient-sharing networks. We searched PubMed, Scopus and IEEE Xplore up to October, 2025, identifying articles that presented networks of at least three healthcare facilities or units connected by observed patient transfers, excluding those restricted to specific patient groups. 79 articles were included from 5,721 screened, describing 50 distinct networks published across diverse disciplines. Overall confidence in study findings, assessed through a modified QuADS tool, was good. Most networks were reconstructed in Europe and North America. Nursing homes and rehabilitation centers were rarely included, and few studies used temporal network approaches. Although most articles computed descriptive statistics and network metrics, only 27 (34%) developed mathematical models of pathogen transmission. Overall, this review highlights the value of patient-sharing networks for understanding and controlling pathogen spread, while emphasizing the need for broader facility inclusion and more temporal modeling.

## Introduction

Inter-institutional patient transfers constitute a key process for healthcare delivery (1). In the United States, 1.4 million patients were transferred between hospitals in 2009, which means that about 4% of all hospital admissions involved a transfer (2). In addition to inter-hospital transfers, patient transfers also frequently occur between units within facilities (3). These movements contribute to the spread of pathogens (4–9). It is therefore necessary to characterize patient transfer patterns, to better understand the spread of communicable diseases in healthcare settings and recommend control strategies.

Social network analysis offers a powerful framework to analyze these systems. It conceptualizes systems as networks composed of nodes connected by edges, enabling visualization and analysis of relationships within the healthcare system. Over the past 15 years, this framework has been used to reconstruct patient-sharing networks in several countries, including the Netherlands, the United Kingdom, the United States, and France (8, 10–16). By analyzing the networks' properties and developing mathematical models of pathogen spread over them, these studies proposed innovative surveillance and control strategies, notably to target antibiotic-resistant bacteria. Previous reviews addressed some aspects of patient-sharing networks. Assab et al. (2017) compiled studies that developed mathematical models of disease transmission across hospital networks (17). DuGoff et al. (2018) reviewed all networks based on shared patient care among physicians, focusing on theoretical frameworks and network reconstruction methods (18). More recently, Korsberg et al. (2024) expanded this concept by analyzing studies that quantified relationships among healthcare providers, which could be either individual clinicians or facilities (19).

However, to our knowledge, no systematic review has specifically examined studies reconstructing networks in which nodes are healthcare facilities or units and edges are patient transfers. This systematic review aims to identify the studies reconstructing such networks, summarize the methodological assumptions underlying network reconstruction, characterize the resulting networks, and describe the network-based analyses, particularly the propagation models developed on the networks.

## Methods
We conducted a systematic review following the PRISMA 2020 guidelines (20). The PRISMA checklist is provided in Table S1. The study protocol was preregistered on June 28, 2024 in PROSPERO (CRD42024559127).

# Search strategy and selection criteria

We searched PubMed, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library for articles published from database inception to October 1, 2025. The search query included three mandatory terms referring to (i) healthcare facilities (e.g. "hospital" or "nursing home"), (ii) patient movements (e.g. "patient transfer" or "patient sharing"), and (iii) the concept of a group of facilities (e.g. "network" or "interhospital"). A fourth term was used to exclude terms that were outside the scope of this review. The detailed search query is provided in Table S2.

# Screening

All results from the search query were independently screened by two reviewers (EH and VR) for eligibility based on the inclusion criteria, using the Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). Any conflicts were resolved by two other investigators (LT and GB).

Screening was first conducted based on titles and abstracts, followed by a full-text assessment. We included only original research articles and excluded reviews, including Cochrane reviews and meta-analyses. We included articles that met the following criteria: (i) used observed data on patient or resident movement, (ii) presented a network constructed from these patient transfer data, (iii) had network nodes representing healthcare facilities or units and edges representing patient transfer, and (iv) presented a network with at least three nodes. We excluded articles that selected patients based on a specific pathology.

# Data extraction

For all included articles, one author (EH) extracted information on the following characteristics: year of publication, journal discipline, network scale, location, patient transfer database and duration. To assess network reconstruction assumptions, we reported patient movement scale (intrafacility, interfacility, or both), types of facilities included, maximum delay between two consecutive hospital stays to be considered as a patient transfer, and network temporality (static or temporal). To evaluate network size, we extracted the number of facilities, number of patient transfers, and the descriptive metrics reported. When disease transmission models were developed, we additionally collected information on the type of model used, the pathogen studied, the interventions simulated, and the epidemiological data integrated.

Because a given network could be described in multiple articles, information was extracted separately each time; similarly, when an article reported more than one network, information was extracted for each network.

Network metrics were classified using an adaptation of the framework proposed by Korsberg et al., 2024 (19): (i) node-level measures, (ii) dyad-level measures, (iii) triad-level measures, and (iv) network-level measures. Node-level measures quantify the position and relationships of individual healthcare facilities or units within a
network. Dyad-level measures describe the connections between pairs of facilities or units, while triad-level
measures assess relationships among groups of three facilities or units. Network-level measures provide an
overall view of the system by summarizing the relationships and positions of all nodes in the network.

## Definitions

In this review, a *network* refers to the structure reconstructed from a specific patient transfer database by a given research team. We considered that a study presented a network if it provided a matrix of transitions between facilities or units, descriptive statistics or a visual representation of the network. An article could describe multiple networks; in such cases, each network was reported separately in this review. Conversely, a given network could be featured in multiple scientific articles, sometimes with minor differences, for instance in the number or type of facilities included.

We distinguished two types of transfers based on the maximum time interval allowed between hospital stays. *Direct transfers* referred to consecutive admissions occurring within a 48-hour time frame, while *indirect transfers* permitted an intermediate stay in the community between two hospitalizations. We classified patient transfers as *intrafacility* when they occurred between units within the same facility or group of facilities, *interfacility* when they occurred between different facilities, and *intrafacility and interfacility* for articles that considered inter-unit movements across multiple facilities. We classified networks as *temporal* when multiple networks were reconstructed at successive time intervals, less than or equal to three months, and *static* otherwise.

## Confidence assessment

The guidelines of the Quality Assessment for Diverse Studies (QuADS) tool used for reporting quality in systematic reviews of mixed- or multi-method studies were adapted to assess the quality and methodological biases of included articles (21). We removed criteria that were not applicable to our review, resulting in a final set of 11 criteria. Articles were rated from 0 to 3 for each criterion (Table S3).

## Results

### Descriptive findings

We found 5,721 articles across PubMed, Scopus and IEEE, of which 2,669 duplicates were removed (Fig. 1). Of the 3,049 abstracts assessed, 511 were selected for full-text screening. Among them, 444 articles were excluded, mainly due to the absence of network reconstruction (n = 357). To identify any additional relevant articles, we examined the references of all included articles, leading to the identification of 12 additional articles meeting inclusion criteria. In total, 79 articles were included in the review.

The included articles were published from 2010 to 2025, with the highest publication frequency occurring in 2017 (Fig. 2A). They were published in journals spanning multiple academic disciplines, most frequently in *Public health, epidemiology, and infectious diseases* journals, which accounted for 35/79 articles (Fig. 2B).
## Most networks were reconstructed in Europe and North America

The 79 included articles presented 50 unique networks (Table S4). Among them, 7/79 (8.9%) articles presented two or more networks (11, 22-28), and 14/50 (28.0%) networks were presented in two or more articles (mean number of articles per network 2, range 1-14). Regarding network scale, 5/50 (10.0%) were national, 33/50 (66.0%) were subnational, and 12/50 (24.0%) comprised only individual hospitals or groups of hospitals (Fig. 3A). Most networks were reconstructed in Europe (26/50, 52.0%) (Fig. 3B) and North America (19/50, 38.0%) (Fig. 3C), while a small number of networks were reconstructed in Asia (3/50, 6.0%) and Oceania (2/50, 4.0%).

## Data sources for network reconstruction

Among the 50 networks, 27 (54.0%) relied on health insurance databases, most of which were public systems, including Programme de médicalisation des systèmes d'information (PMSI) in France and Medicare in the United States (16, 29) (Fig. 4A), and one network was reconstructed with a combination of health insurance and survey databases (29). Database durations ranged from 3 months to 9 years, with a median duration of 2 years (Fig. 4B). Figure S1 presents the types and durations of the databases used in the 79 articles separately, showing similar results.

## Most networks included only hospitals and were static rather than temporal

Most networks considered exclusively hospitals (39/50, 78.0%), excluding other types of healthcare facilities such as nursing homes or rehabilitation centres (Fig. 5). Most articles focused on interfacility transfers (36/50, 72.0%), while 12/50 (24.0%) considered intrafacility movements and 2/50 (4.0%) combined both. 27/50 (54.0%) networks included only direct transfers, while 23/50 (46.0%) networks included indirect transfers. The maximum interval allowed between two consecutive stays varied between 10 days and 8 years, with 6/50 (12.0%) networks allowing for more than a year between two hospital stays (Table S5). Most articles reconstructed a single static network (40/50, 80.0%), regardless of the type of movement or scale considered. All articles that focused exclusively on intrafacility movements considered only direct transfers. All articles that incorporated facilities beyond single hospitals or hospital groups addressed interfacility or mixed movements.

## Network facility counts varied strongly across scales

National networks included a median of 826 facilities (2.5-97.5% quantiles: 60-5,036), subnational networks 116 facilities (12-504), and hospital-level networks 58 units (29-340) (Fig. 6A). 2/79 articles did not report the number of facilities included in their network (30, 31). Figure S2 presents the network characteristics for each article separately and shows that the facility counts were generally consistent across articles presenting a given network.

## Patient transfer counts varied according to transfer type

The median number of patient transfers was 14,347 (2.5-97.5% quantiles: 1,205-821,293) when considering only direct transfers and 46,659 (634-1,140,436) when indirect transfers were included (Fig. 6B). Patient
transfer counts were not reported in 46/79 (58.2%) articles. Among them, some described their database using alternative measures such as the total number of unique patients (26–28, 30–36) or the total number of admissions to healthcare facilities (24, 26, 29, 30, 33, 35, 37, 38).

## Network descriptive metrics

Overall, 59/79 (74.7%) articles used at least one metric to describe their network (Fig. 7). The most used metrics were density, degree and its variations including in-degree and out-degree. In addition, 13/79 (16.4%) used a community detection method to identify clusters within their network. For greater detail, Table S6 lists all metrics reported in the included articles.

## Epidemiological use of the networks

Overall, 27/79 (35.4%) articles developed a mathematical model of pathogen transmission. 23/27 (74.2%) models focused on a specific pathogen, most frequently *S. aureus* (11/27, 40.7%) and Enterobacterales (5/27, 18.5%) (Fig. 8A). 17/27 (63.0%) models were agent-based, and 10/27 (37.0%) models were compartmental. 12/27 (44.4%) models integrated epidemiological data, and 12/27 (44.4%) articles simulated at least one public health intervention (Fig. 8B).

Regarding public health interventions, 8/27 (29.6%) articles simulated the screening of a fraction of incoming patients with isolation of positives, generally suggesting potential reductions in pathogen prevalence (6, 24, 26, 38–41); for instance, screening 30 to 40% of patients could potentially halve norovirus prevalence within 100 days (42). 3/27 (11.1%) articles simulated a sentinel surveillance system (11, 36, 43), and indicated that a small number of sentinel hospitals might be sufficient to shorten detection times (11) and effectively monitor *S. aureus* and Enterobacterales spread (36, 43). Interestingly, higher rates of inter-hospital patient transfer tended to dilute rather than amplify pathogen prevalence (24). Shorter lengths of stay and reduced readmission rates seem to lower pathogen prevalence (24, 38). The impact of contact precautions was pathogen-specific, reducing norovirus transmission (42) but showing minimal effects on MRSA (38).

## Confidence assessment

Overall, the articles were based on a clear conceptual basis, with well-defined aims, thorough descriptions of the research setting, good adequacy of the research setting in relation to the stated aims, and a good description of strengths and limits (Fig. 9). Although the rationale for selecting the patient transfer databases was not always clearly articulated (32/79, 40.5% with a rating of 0 or 1), the adequacy of the data sources was generally good. The criterion most frequently unmet by the articles was the accessibility of the underlying recruitment data: most articles either used descriptive statistics (36/79, 45.6% with a rating of 0) or did not describe their patient transfer database (41/79, 51.9% with a rating of 0), which limited the understanding of these datasets. Although the methods used to analyze the network were generally adequate, some articles did not justify their methodological choices (36/79, 45.6% with a rating of 0 or 1). Most articles demonstrated insufficient stakeholder involvement in the design and analysis processes (66/79, 83.5% with a rating of 1). The scores for each included article are detailed in Table S7.

## Discussion

### Review findings
Our systematic review highlights the value of social network analysis for investigating patient pathways. The included articles were published in journals from diverse fields, including public health, social sciences and mathematics. This interdisciplinarity can provide multiple perspectives that enhance our understanding of patient-sharing networks. Additionally, most networks were reconstructed in Europe and North America, while other continents were underrepresented. Few networks included types of facilities other than acute care hospitals, including nursing homes and rehabilitation centres, and few networks were temporal. The network-based analyses varied substantially between the articles, including descriptive statistics, descriptive metrics, and mathematical models of pathogen transmission.

# Implications for future work

This review raised several points that can inform future works.

First, most networks were reconstructed in Europe or North America, which limits our understanding of patient-sharing networks in other regions of the world. This geographic imbalance may partly reflect unequal access to patient transfer databases. In low-resource settings, very few large-scale patient-stay databases are available (31, 44–46), whereas in Europe and North America, large administrative datasets have enabled the reconstruction of comprehensive healthcare facility networks (14). Even within Europe, substantial differences exist between countries. For example, in Germany, around 10% of the population is privately insured and 90% of the population is insured through the public health insurance system, which is regionally fragmented due to the country's federal structure. This fragmentation limits the possibility of reconstructing a comprehensive national network, as reconstructions of region-specific networks would miss cross-border patient transfers (6, 7, 24, 26–28, 35, 39).

Second, the quality and comprehensiveness of patient transfer databases varied substantially across articles. Several articles acknowledged that their databases were not exhaustive (31, 37, 47), which can compromise network quality, even if certain analytic approaches may mitigate this issue (27). The extent of missing data depends on the type of database used for network reconstruction. Di Vincenzo (2018) suggested that administrative databases are more precise, detailed, and complete than survey-based databases (48); these sources should therefore be prioritized whenever accessible.

Third, regarding methodological choices in network reconstruction, most networks focused exclusively on hospitals, excluding other types of healthcare facilities. However, these institutions are strongly interconnected with hospitals and can act as important pathogen reservoirs, which makes their inclusion particularly relevant in articles modelling pathogen transmission. Notably, nursing homes have been identified as major reservoirs of antibiotic-resistant pathogens (8, 12, 29, 40, 49–52). When such data are available, they should therefore be integrated into the network. In addition, most articles considered only interfacility transfers, while patterns of patient transfers within facilities are also important to study, as they can strongly affect hospital performance and contribute to increased infection risk (26, 30, 53–56). Some articles did not include indirect patient transfers, despite the fact that these can represent up to 94–99% of all transfers depending on the allowed interval between two hospital stays (6, 7, 39, 57), and can significantly increase pathogen prevalence (7). Decisions about whether to include indirect transfers, and the maximal allowed duration between two stays, therefore have a major impact on the resulting network structure and predicted pathogen spread across healthcare facilities. They may be informed by data on the pathogen of interest's
natural history of infection; for instance, studies focusing on pathogens with short infectious durations (e.g.,
influenza) may only include direct transfers, while studies aiming to understand the spread of pathogens that
can colonize humans for months (e.g., MRSA) should account for indirect transfers. Moreover, most articles
reconstructed a single static network using the entire period available in their database. While static networks
provide a global overview of patient flows, temporal networks capture fluctuations over time, such as weekly
or seasonal variations, including higher flows in winter due to respiratory viruses (58). As noted by Belik et al.
(2017), purely static networks can incorrectly estimate transmission risks by assuming all transfer links exist
at all times (39).

Fourth, several networks were presented by multiple articles, some of which relied on different subsets of the
same underlying network. As a result, their reconstructed forms varied in size (5, 59), in the types of facilities
included (29, 59), in whether indirect transfers were included (35, 36), and in the time periods covered by the
patient transfer data (23, 54). These differences show that, once reconstructed, a network can evolve through
methodological choices to address different research questions.

Limitations

Our work has several limitations. First, the articles we identified come from a wide range of disciplines, which
sometimes use different terminologies to describe the same methods, potentially leading to the omission of
some relevant articles. To reduce this risk, we included as many synonyms as possible in our search strategy
and systematically reviewed the reference lists of all included articles. Second, we voluntarily excluded large
databases giving access to grey literature (e.g. Google Scholar), to only retain original research articles in this
review, although this kind of database could have provided additional networks. Third, we excluded networks
constructed from data on patients with specific diseases, in order to draw conclusions about overall patient-
sharing networks. However, this approach may have led us to overlook highly structured networks, such as
those based on patients with carbapenem-resistant Enterobacterales (60) or with hypertension (61). Fourth,
we defined a network as "temporal" when at least one instance was observed per quarter. Although this
threshold is somewhat arbitrary, it was selected to account for seasonal variations; alternative definitions
might have yielded slightly different results. Fifth, when assessing pathogen transmission modelling
approaches, we excluded statistical modelling articles because they did not align with the primary objective of
our review. Nonetheless, such models could provide valuable insights into the transmission dynamics of
pathogens in healthcare settings. Sixth, our review focused exclusively on patient transfers between
healthcare facilities or units. However, hospitals collaborate in other ways as well (62). Studies have
investigated collaborations through the mobility of healthcare professionals across institutions (63) or the
sharing of patients among physicians (64). Within-ward networks where nodes represent hospital rooms have
also been proposed to better understand the patterns of patient movements and staff within hospitals (65).

Perspectives and future directions

Future work should expand healthcare network reconstruction to include a broader range of facility types, and
reconstruct more temporal networks, particularly when these networks are used in parallel to pathogen
transmission models. Pathogen transmission models could focus on emerging threats including Carbapenem-
Resistant Enterobacterales and Candida auris. In addition, improving data transparency, especially by giving
access to the transition matrices used to reconstruct the networks, would strengthen the reproducibility and
support open science. Finally, to date, few studies have attempted to integrate genomic data into patient-
sharing networks. Such integration could reveal clusters of facilities sharing genetically close strains, and
connect them to patient transfer patterns, which could provide a better understanding of pathogen
transmission pathways across healthcare systems.

Abbreviations

HAI : Healthcare-associated infection

IEEE : Institute of Electrical and Electronics Engineers

PMSI: Programme de Médicalisation des Systèmes d'Information

QuADS : Quality Assessment for Diverse Studies

Declarations

**Acknowledgements:** None

**Declaration of interests:** The authors declare that they have no conflict of interest.

**Funding:** This study was supported by the French state through the Agence Nationale de la Recherche (ANR 22 PAMR 0003, France 2030). The sponsors had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

**Ethical review statement:** According to the French law, no ethical review was needed to conduct this study.

Authors' contributions:

G.B., E.H. and L.T. conceptualized the study and led the design of the systematic review. P.Clin, E.H. and V.R. performed the literature search. E.H. performed the data extraction and quality assessment. G.B., P.Crépey, P. Clin, E.H. and L.T. interpreted the results. E.H. drafted the manuscript. All authors reviewed and approved the final manuscript.

Availability of data and materials:

All code used in this review are available in the following GitHub repository:
https://github.com/elisehodbert/systematic_review

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## Figures

<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
Flow diagram of study selection.

<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>

Characteristics of included articles. (A) Annual publication counts (B) Distribution across journal categories.
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Figure 3</figcaption></figure>

Scale and geographic distribution of the 50 networks. (A) Number of networks by scale (B) European networks (C) North American networks. Numbers in parentheses indicate how many articles reported each network if more than one.
<figure><img src="image_7.png" /><figcaption>Figure 4</figcaption></figure>

Types and duration of databases in the networks. (A) Frequency of database types. (B) Distribution of database duration.

<figure><img src="image_8.png" /><figcaption>Figure 5</figcaption></figure>

Methodological choices for network reconstruction. Networks were described as including "Hospital and other healthcare facilities" when they were presented as including facilities other than hospitals in at least one
article. Patient transfers were labeled "Indirect" when at least one article presenting the network allowed for a stay in the community between two hospital stays. Networks were considered "Temporal" when a temporal reconstruction was used in at least one article.

<figure><img src="image_9.png" /><figcaption>Figure 6</figcaption></figure>

Network characteristics. (A) Number of facilities (B) Number of patient transfers. For networks described in multiple articles, the highest reported value is shown.
<figure><img src="image_10.png" /><figcaption>Figure 7</figcaption></figure>

Reported network metrics in the included articles, categorized by analytical level (node-, dyad-, triad-, and network-level measures). Color gradients indicate the number of occurrences.
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Figure 8</figcaption></figure>

Interventions simulated in the included articles.
<figure><img src="image_13.png" /><figcaption>Figure 9</figcaption></figure>

*Confidence assessment criteria of included articles adapted from Harrison et al., 2021 (21)*

**Supplementary Files**

This is a list of supplementary files associated with this preprint. Click to download.

* SupplementalMaterial.docx