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Optimising spatial accessibility to inform rationalisation of specialist health services
BACKGROUND: In an era of budget constraints for healthcare services, strategies for provision of services that improve quality whilst saving costs are highly valued. A proposed means to achieve this is consolidation of services into fewer specialist centres, but this may lead to reduced spatial acce...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5399864/ https://www.ncbi.nlm.nih.gov/pubmed/28431545 http://dx.doi.org/10.1186/s12942-017-0088-6 |
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author | Smith, Catherine M. Fry, Hannah Anderson, Charlotte Maguire, Helen Hayward, Andrew C. |
author_facet | Smith, Catherine M. Fry, Hannah Anderson, Charlotte Maguire, Helen Hayward, Andrew C. |
author_sort | Smith, Catherine M. |
collection | PubMed |
description | BACKGROUND: In an era of budget constraints for healthcare services, strategies for provision of services that improve quality whilst saving costs are highly valued. A proposed means to achieve this is consolidation of services into fewer specialist centres, but this may lead to reduced spatial accessibility. We describe a methodology which includes implementing a combinatorial optimisation algorithm to derive combinations of services which optimise spatial accessibility in the context of service rationalisation, and demonstrate its use through the exemplar of tuberculosis clinics in London. METHODS: Our methodology involves (1) identifying the spatial distribution of the patient population using the service; (2) calculating patient travel times to each service location, and (3) using a combinatorial optimisation algorithm to identify subsets of locations that minimise overall travel time. We estimated travel times for tuberculosis patients notified in London between 2010 and 2013 to each of 29 clinics in the city. Travel time estimates were derived from the Transport for London Journey Planner service. We identified the subset of clinics that would provide the shortest overall travel time for each possible number of clinic subsets (1–28). RESULTS: Based on the 29 existing clinic locations, mean estimated travel time to clinics used by 12,061 tuberculosis patients in London was 33 min; and mean time to their nearest clinics was 28 min. Using optimum combinations of clinic locations, and assuming that patients attended their nearest clinics, a mean travel time of less than 45 min could be achieved with three clinics; of 34 min with ten clinics, and of less than 30 min with 18 clinics. CONCLUSIONS: We have developed a methodological approach to optimise spatial accessibility which can be used to inform rationalisation of health services. In urban conurbations, this may enable service reorganisation which increases quality and efficiency without substantially affecting spatial accessibility. This approach could be used to inform planning of service reorganisations, but may not be generalisable to rural areas or smaller urban centres. |
format | Online Article Text |
id | pubmed-5399864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53998642017-04-24 Optimising spatial accessibility to inform rationalisation of specialist health services Smith, Catherine M. Fry, Hannah Anderson, Charlotte Maguire, Helen Hayward, Andrew C. Int J Health Geogr Methodology BACKGROUND: In an era of budget constraints for healthcare services, strategies for provision of services that improve quality whilst saving costs are highly valued. A proposed means to achieve this is consolidation of services into fewer specialist centres, but this may lead to reduced spatial accessibility. We describe a methodology which includes implementing a combinatorial optimisation algorithm to derive combinations of services which optimise spatial accessibility in the context of service rationalisation, and demonstrate its use through the exemplar of tuberculosis clinics in London. METHODS: Our methodology involves (1) identifying the spatial distribution of the patient population using the service; (2) calculating patient travel times to each service location, and (3) using a combinatorial optimisation algorithm to identify subsets of locations that minimise overall travel time. We estimated travel times for tuberculosis patients notified in London between 2010 and 2013 to each of 29 clinics in the city. Travel time estimates were derived from the Transport for London Journey Planner service. We identified the subset of clinics that would provide the shortest overall travel time for each possible number of clinic subsets (1–28). RESULTS: Based on the 29 existing clinic locations, mean estimated travel time to clinics used by 12,061 tuberculosis patients in London was 33 min; and mean time to their nearest clinics was 28 min. Using optimum combinations of clinic locations, and assuming that patients attended their nearest clinics, a mean travel time of less than 45 min could be achieved with three clinics; of 34 min with ten clinics, and of less than 30 min with 18 clinics. CONCLUSIONS: We have developed a methodological approach to optimise spatial accessibility which can be used to inform rationalisation of health services. In urban conurbations, this may enable service reorganisation which increases quality and efficiency without substantially affecting spatial accessibility. This approach could be used to inform planning of service reorganisations, but may not be generalisable to rural areas or smaller urban centres. BioMed Central 2017-04-21 /pmc/articles/PMC5399864/ /pubmed/28431545 http://dx.doi.org/10.1186/s12942-017-0088-6 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Smith, Catherine M. Fry, Hannah Anderson, Charlotte Maguire, Helen Hayward, Andrew C. Optimising spatial accessibility to inform rationalisation of specialist health services |
title | Optimising spatial accessibility to inform rationalisation of specialist health services |
title_full | Optimising spatial accessibility to inform rationalisation of specialist health services |
title_fullStr | Optimising spatial accessibility to inform rationalisation of specialist health services |
title_full_unstemmed | Optimising spatial accessibility to inform rationalisation of specialist health services |
title_short | Optimising spatial accessibility to inform rationalisation of specialist health services |
title_sort | optimising spatial accessibility to inform rationalisation of specialist health services |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5399864/ https://www.ncbi.nlm.nih.gov/pubmed/28431545 http://dx.doi.org/10.1186/s12942-017-0088-6 |
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