Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/137222
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Type: Journal article
Title: Modular structures and the delivery of inpatient care in hospitals: a Network Science perspective on healthcare function and dysfunction
Author: Ben-Tovim, D.I.
Bajger, M.
Bui, V.D.
Qin, S.
Thompson, C.H.
Citation: BMC Health Services Research, 2022; 22(1):1-12
Publisher: Springer Science and Business Media LLC
Issue Date: 2022
ISSN: 1472-6963
1472-6963
Statement of
Responsibility: 
David I. Ben, Tovim, Mariusz Bajger, Viet Duong Bui, Shaowen Qin, and Campbell H. Thompson
Abstract: Background: Reinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical human and physical components interacting in diverse ways in varying locations. It is challeng‑ ing to represent the function and dysfunction of such systems in a scientifc manner. We describe a Network Science approach to that dilemma. General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary Network Science. We explore that possibility in a study of Australian hospitals during 2019 and 2020. Methods: We accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisations inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care. Results: We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identifcation of community structures confrmed the modular structure of the graphs. Their functional implications were readily identifed by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion. Discussion and conclusions: Contemporary Network Science is one of the fastest growing areas of current scientifc and technical advance. Network Science confrms the modular nature of healthcare service structures. It holds con‑ siderable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.
Keywords: Network graphs; Network science; Modularity; Healthcare services provision; Congestion; Covid-19
Rights: © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
DOI: 10.1186/s12913-022-08865-8
Published version: http://dx.doi.org/10.1186/s12913-022-08865-8
Appears in Collections:Medicine publications

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