An interactive geographic information system to inform optimal locations for healthcare services.


Journal

PLOS digital health
ISSN: 2767-3170
Titre abrégé: PLOS Digit Health
Pays: United States
ID NLM: 9918335064206676

Informations de publication

Date de publication:
May 2023
Historique:
received: 09 10 2022
accepted: 11 04 2023
medline: 8 5 2023
pubmed: 8 5 2023
entrez: 8 5 2023
Statut: epublish

Résumé

Large health datasets can provide evidence for the equitable allocation of healthcare resources and access to care. Geographic information systems (GIS) can help to present this data in a useful way, aiding in health service delivery. An interactive GIS was developed for the adult congenital heart disease service (ACHD) in New South Wales, Australia to demonstrate its feasibility for health service planning. Datasets describing geographic boundaries, area-level demographics, hospital driving times, and the current ACHD patient population were collected, linked, and displayed in an interactive clinic planning tool. The current ACHD service locations were mapped, and tools to compare current and potential locations were provided. Three locations for new clinics in rural areas were selected to demonstrate the application. Introducing new clinics changed the number of rural patients within a 1-hour drive of their nearest clinic from 44·38% to 55.07% (79 patients) and reduced the average driving time from rural areas to the nearest clinic from 2·4 hours to 1·8 hours. The longest driving time was changed from 10·9 hours to 8·9 hours. A de-identified public version of the GIS clinic planning tool is deployed at https://cbdrh.shinyapps.io/ACHD_Dashboard/. This application demonstrates how a freely available and interactive GIS can be used to aid in health service planning. In the context of ACHD, GIS research has shown that adherence to best practice care is impacted by patients' accessibility to specialist services. This project builds on this research by providing opensource tools to build more accessible healthcare services.

Identifiants

pubmed: 37155602
doi: 10.1371/journal.pdig.0000253
pii: PDIG-D-22-00277
pmc: PMC10166531
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0000253

Informations de copyright

Copyright: © 2023 Nicholson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of the manuscript have the following competing interests: DC and CN are part of the Congenital Heart Alliance of Australia and New Zealand, whose currently work involves the development of a Congenital Heart Disease Registry for Australia and New Zealand. Whilst the current study was not part of this affiliation, future development of the methodologies described here will likely make use of this resource.

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Auteurs

Calum Nicholson (C)

Central Clinical School, University of Sydney Faculty of Medicine and Health, Darlington, New South Wales, Australia.
Clinical Research Group, Heart Research Institute, Newtown, New South Wales, Australia.
Cardiology Department, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia.

Mark Hanly (M)

Centre for Big Data Research in Health, University of New South Wales, Kensington, New South Wales, Australia.

David S Celermajer (DS)

Central Clinical School, University of Sydney Faculty of Medicine and Health, Darlington, New South Wales, Australia.
Clinical Research Group, Heart Research Institute, Newtown, New South Wales, Australia.
Cardiology Department, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia.

Classifications MeSH