Optimal administrative geographies: an algorithmic approach

D. Datta, J.R. Figueira, A.M. Gourtani, A. Morton

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Centrally planned Beveridge healthcare systems typically rely heavily on local or regional "health authorities" as responsible organisations for the care of geographically defined populations. The frequency of reorganisations in the English NHS suggests that there is no compelling unitary definition of what constitutes a good healthcare geography. In this paper we propose a set of desirable objectives for an administrative healthcare geography, specifically: geographical compactness, co-extensiveness with current local authorities and size and population homogeneity, and we show how these might be operationally measured. Based on these objectives, we represent the problem of how to partition a territory into health authorities as a multi-objective optimisation problem. We use a state-of-the-art multi-objective genetic algorithm customised for the needs of our study to partition the territory of the East England into 14 Primary Care Trusts and 50GP consortia and study the tradeoffs between objectives which this reveals.
LanguageEnglish
Pages247-257
Number of pages11
JournalSocio-Economic Planning Sciences
Volume47
Issue number3
DOIs
Publication statusPublished - 1 Sep 2013

Fingerprint

Geography
Healthcare
health care
geography
Health
Partition
Primary Care
Multi-objective Genetic Algorithm
Multiobjective Optimization Problems
genetic algorithm
Homogeneity
homogeneity
Compactness
Trade-offs
health
reorganization
Authority

Keywords

  • Primary Care Trusts
  • multi-objective optimisation
  • genetic algorithm
  • healthcare geography
  • GP consortium

Cite this

Datta, D. ; Figueira, J.R. ; Gourtani, A.M. ; Morton, A. / Optimal administrative geographies : an algorithmic approach. In: Socio-Economic Planning Sciences . 2013 ; Vol. 47, No. 3. pp. 247-257.
@article{962a6c66bf244666a5a47fbaadf4da29,
title = "Optimal administrative geographies: an algorithmic approach",
abstract = "Centrally planned Beveridge healthcare systems typically rely heavily on local or regional {"}health authorities{"} as responsible organisations for the care of geographically defined populations. The frequency of reorganisations in the English NHS suggests that there is no compelling unitary definition of what constitutes a good healthcare geography. In this paper we propose a set of desirable objectives for an administrative healthcare geography, specifically: geographical compactness, co-extensiveness with current local authorities and size and population homogeneity, and we show how these might be operationally measured. Based on these objectives, we represent the problem of how to partition a territory into health authorities as a multi-objective optimisation problem. We use a state-of-the-art multi-objective genetic algorithm customised for the needs of our study to partition the territory of the East England into 14 Primary Care Trusts and 50GP consortia and study the tradeoffs between objectives which this reveals.",
keywords = "Primary Care Trusts, multi-objective optimisation, genetic algorithm, healthcare geography, GP consortium",
author = "D. Datta and J.R. Figueira and A.M. Gourtani and A. Morton",
year = "2013",
month = "9",
day = "1",
doi = "10.1016/j.seps.2013.03.002",
language = "English",
volume = "47",
pages = "247--257",
journal = "Socio-Economic Planning Sciences",
issn = "0038-0121",
number = "3",

}

Optimal administrative geographies : an algorithmic approach. / Datta, D.; Figueira, J.R.; Gourtani, A.M.; Morton, A.

In: Socio-Economic Planning Sciences , Vol. 47, No. 3, 01.09.2013, p. 247-257.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimal administrative geographies

T2 - Socio-Economic Planning Sciences

AU - Datta, D.

AU - Figueira, J.R.

AU - Gourtani, A.M.

AU - Morton, A.

PY - 2013/9/1

Y1 - 2013/9/1

N2 - Centrally planned Beveridge healthcare systems typically rely heavily on local or regional "health authorities" as responsible organisations for the care of geographically defined populations. The frequency of reorganisations in the English NHS suggests that there is no compelling unitary definition of what constitutes a good healthcare geography. In this paper we propose a set of desirable objectives for an administrative healthcare geography, specifically: geographical compactness, co-extensiveness with current local authorities and size and population homogeneity, and we show how these might be operationally measured. Based on these objectives, we represent the problem of how to partition a territory into health authorities as a multi-objective optimisation problem. We use a state-of-the-art multi-objective genetic algorithm customised for the needs of our study to partition the territory of the East England into 14 Primary Care Trusts and 50GP consortia and study the tradeoffs between objectives which this reveals.

AB - Centrally planned Beveridge healthcare systems typically rely heavily on local or regional "health authorities" as responsible organisations for the care of geographically defined populations. The frequency of reorganisations in the English NHS suggests that there is no compelling unitary definition of what constitutes a good healthcare geography. In this paper we propose a set of desirable objectives for an administrative healthcare geography, specifically: geographical compactness, co-extensiveness with current local authorities and size and population homogeneity, and we show how these might be operationally measured. Based on these objectives, we represent the problem of how to partition a territory into health authorities as a multi-objective optimisation problem. We use a state-of-the-art multi-objective genetic algorithm customised for the needs of our study to partition the territory of the East England into 14 Primary Care Trusts and 50GP consortia and study the tradeoffs between objectives which this reveals.

KW - Primary Care Trusts

KW - multi-objective optimisation

KW - genetic algorithm

KW - healthcare geography

KW - GP consortium

UR - http://www.scopus.com/inward/record.url?scp=84881557119&partnerID=8YFLogxK

U2 - 10.1016/j.seps.2013.03.002

DO - 10.1016/j.seps.2013.03.002

M3 - Article

VL - 47

SP - 247

EP - 257

JO - Socio-Economic Planning Sciences

JF - Socio-Economic Planning Sciences

SN - 0038-0121

IS - 3

ER -